Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Prediction of Drug-Drug Interactions with Ensartinib as a Time-Dependent CYP3A Inhibitor Using Physiologically Based Pharmacokinetic Model

Ensartinib (X-396) is a second-generation anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI) indicated for the treatment of ALK-positive patients with locally advanced or metastatic non-small cell lung cancer. Although in vitro experiments and molecular docking suggested its potential as a cytochrome P450 inhibitor, no further investigation or clinical trials have been conducted to assess its drug-drug interaction (DDI) risk. In this study, we conducted a series of in vitro experiments to elucidate the inhibition mechanism of ensartinib. Furthermore, a physiologically-based pharmacokinetic (PBPK) model was developed based on in vitro, in silico, and in vivo parameters, verified using clinical data, and applied to predict the clinical DDI mediated by ensartinib. The in vitro incubation experiments suggested that ensartinib exhibited strong time-dependent inhibition. Simulation results from the PBPK model indicated a significant increase in the exposure of CYP3A substrates in the presence of ensartinib, with the maximal plasma concentration and area under the plasma concentration-time curve increasing up to 12-fold and 29-fold for sensitive substrates. Based on these findings, it is evident that co-administration of ensartinib and CYP3A substrates requires careful regulatory consideration. SIGNIFICANCE STATEMENT: Ensartinib was found to be a strong time-dependent inhibitor of CYP3A for the first time based on in vitro experiments, but there was no research conducted to estimate the risk of drug-drug interaction (DDI) of ensartinib in clinic. Therefore, the first ensartinib physiologically based pharmacokinetic model was developed and applied to predict various untested scenarios. The simulation result indicated that the exposure of CYP3A substrate increased significantly and urged the further clinical DDI study.

Physiologically-Based Pharmacokinetic Modeling for Drug Dosing in Pediatric Patients: A Tutorial for a Pragmatic Approach in Clinical Care

It is well-accepted that off-label drug dosing recommendations for pediatric patients should be based on the best available evidence. However, the available traditional evidence is often low. To bridge this gap, physiologically-based pharmacokinetic (PBPK) modeling is a scientifically well-founded tool that can be used to enable model-informed dosing (MID) recommendations in children in clinical practice. In this tutorial, we provide a pragmatic, PBPK-based pediatric modeling workflow. For this approach to be successfully implemented in pediatric clinical practice, a thorough understanding of the model assumptions and limitations is required. More importantly, careful evaluation of an MID approach within the context of overall benefits and the potential risks is crucial. The tutorial is aimed to help modelers, researchers, and clinicians, to effectively use PBPK simulations to support pediatric drug dosing.

In vitro to in vivo extrapolation for predicting human equivalent dose of phenolic endocrine disrupting chemicals: PBTK model development, biological pathways, outcomes and performance

In vitro to in vivo (IVIVE) leverages in vitro high-throughput biological responses to predict the corresponding in vivo exposures and further estimate the human safe dose. However, for phenolic endocrine disrupting chemicals (EDCs) linked with complicated biological pathways and adverse outcomes (AO), such as bisphenol A (BPA) and 4-nonylphenol (4-NP), plausible estimation of human equivalent doses (HED) by IVIVE approaches considering various biological pathways and endpoints is still challenging. To explore the capabilities and limitations of IVIVE, this study conducted physiologically based toxicokinetic (PBTK)-IVIVE approaches to derive pathway-specific HEDs using BPA and 4-NP as examples. In vitro HEDs of BPA and 4-NP varied in different adverse outcomes, pathways, and testing endpoints and ranged from 0.0013 to 1.0986 mg/kg bw/day and 0.0551 to 1.7483 mg/kg bw/day, respectively. In vitro HEDs associated with reproductive AOs initiated by PPARα activation and ER agonism were the most sensitive. Model verification suggested the potential of using effective in vitro data to determine reasonable approximation of in vivo HEDs for the same AO (fold differences of most AOs ranged in 0.14-2.74 and better predictions for apical endpoints). Furthermore, system-specific parameters of cardiac output and its fraction, body weight, as well as chemical-specific parameters of partition coefficient and liver metabolic were most sensitive for the PBTK simulations. The results indicated that the application of fit for-purpose PBTK-IVIVE approach could provide credible pathway-specific HEDs and contribute to high throughput prioritization of chemicals in a more realistic scenario.

Physiologically based pharmacokinetic modeling to characterize enterohepatic recirculation and predict food effect on the pharmacokinetics of hyzetimibe

BACKGROUND AND OBJECTIVE

Hyzetimibe is a cholesterol absorption inhibitor indicated for the treatment of hypercholesterolemia. This study aims to describe the multiple-peak pharmacokinetics (PK) of hyzetimibe and its active metabolite M1 through physiologically-based pharmacokinetic (PBPK) modeling, and to compare the model predictions of a virtual food effect study with the results of a clinical food effect study.

METHODS

The plasma concentration data used for PBPK modeling were obtained from a single-dose, two-period crossover bioequivalence study in the fasted state. Advanced Compartmental Absorption and Transit model was used for absorption. Enterohepatic recirculation process was modeled by changing the gut physiological state from fasted to fed at meal time. Based on the established PBPK models, a virtual food effect study was simulated. A clinical food effect study was used for model external validation.

RESULTS

PK profiles of hyzetimibe and M1 under fasting condition could be well described by the PBPK model, and the errors of Cmax, AUC0-∞, and AUC0-t were within the two-fold range. Simulated geometric mean ratios (GMRs, fed/fasted) showed that a high-fat breakfast slightly affected the PK of hyzetimibe, expressed as increased Cmax of hyzetimibe (130.6%). Simulated GMRs and 90% confidence intervals of AUC were within the preset bioequivalent range. The results of the simulated virtual food effect trial were consistent with those of the clinical food effect trial.

CONCLUSIONS

The established PBPK model could describe the concentration-time profiles of hyzetimibe and M1 well with good prediction performance. A fully mechanistic model of enterohepatic recirculation warrants further investigation.

Computational Amendment of Parenteral In Situ Forming Particulates' Characteristics: Design of Experiment and PBPK Physiological Modeling

Lipid and/or polymer-based drug conjugates can potentially minimize side effects by increasing drug accumulation at target sites and thus augment patient compliance. Formulation factors can present a potent influence on the characteristics of the obtained systems. The selection of an appropriate solvent with satisfactory rheological properties, miscibility, and biocompatibility is essential to optimize drug release. This work presents a computational study of the effect of the basic formulation factors on the characteristics of the obtained in situ-forming particulates (IFPs) encapsulating a model drug using a 21.31 full factorial experimental design. The emulsion method was employed for the preparation of lipid and/or polymer-based IFPs. The IFP release profiles and parameters were computed. Additionally, a desirability study was carried out to choose the optimum formulation for further morphological examination, rheological study, and PBPK physiological modeling. Results revealed that the type of particulate forming agent (lipid/polymer) and the incorporation of structure additives like Brij 52 and Eudragit RL can effectively augment the release profile as well as the burst of the drug. The optimized formulation exhibited a pseudoplastic rheological behavior and yielded uniformly spherical-shaped dense particulates with a PS of 573.92 ± 23.5 nm upon injection. Physiological modeling simulation revealed the pioneer pharmacokinetic properties of the optimized formulation compared to the observed data. These results assure the importance of controlling the formulation factors during drug development, the potentiality of the optimized IFPs for the intramuscular delivery of piroxicam, and the reliability of PBPK physiological modeling in predicting the biological performance of new formulations with effective cost management.

Utility of Minimal Physiologically Based Pharmacokinetic Models for Assessing Fractional Distribution, Oral Absorption, and Series-Compartment Models of Hepatic Clearance

Minimal physiologically based pharmacokinetic (mPBPK) models are physiologically relevant, require less information than full PBPK models, and offer flexibility in pharmacokinetics (PK). The well-stirred hepatic model (WSM) is commonly used in PBPK, whereas the more plausible dispersion model (DM) poses computational complexities. The series-compartment model (SCM) mimics the DM but is easier to operate. This work implements the SCM and mPBPK models for assessing fractional tissue distribution, oral absorption, and hepatic clearance using literature-reported blood and liver concentration-time data in rats for compounds mainly cleared by the liver. Further handled were various complexities, including nonlinear hepatic binding and metabolism, differing absorption kinetics, and sites of administration. The SCM containing one to five (n) liver subcompartments yields similar fittings and provides comparable estimates for hepatic extraction ratio (ER), prehepatic availability (Fg ), and first-order absorption rate constants (ka ). However, they produce decreased intrinsic clearances (CLint ) and liver-to-plasma partition coefficients (Kph ) with increasing n as expected. Model simulations demonstrated changes in intravenous and oral PK profiles with alterations in Kph and ka and with hepatic metabolic zonation. The permeability (PAMPA P) of the various compounds well explained the fitted fractional distribution (fd ) parameters. The SCM and mPBPK models offer advantages in distinguishing systemic, extrahepatic, and hepatic clearances. The SCM allows for incorporation of liver zonation and is useful in assessing changes in internal concentration gradients potentially masked by similar blood PK profiles. Improved assessment of intraorgan drug concentrations may offer insights into active moieties driving metabolism, biliary excretion, pharmacodynamics, and hepatic toxicity. SIGNIFICANCE STATEMENT: The minimal physiologically based pharmacokinetic model and the series-compartment model are useful in assessing oral absorption and hepatic clearance. They add flexibility in accounting for various drug- or system-specific complexities, including fractional distribution, nonlinear binding and saturable hepatic metabolism, and hepatic zonation. These models can offer improved insights into the intraorgan concentrations that reflect physiologically active moieties often driving disposition, pharmacodynamics, and toxicity.

Physiologically based pharmacokinetic modelling of semaglutide in children and adolescents with healthy and obese body weights

AIMS

To develop paediatric physiologically based pharmacokinetic modelling (PBPK) models of semaglutide to estimate the pharmacokinetic profile for subcutaneous injections in children and adolescents with healthy and obese body weights.

METHODS

Pharmacokinetic modelling and simulations of semaglutide subcutaneous injections were performed using the Transdermal Compartmental Absorption & Transit model implemented in GastroPlus v.9.5 modules. A PBPK model of semaglutide was developed and verified in the adult population, by comparing the simulated plasma exposure with the observed data, and further scaled to the paediatric populations with normal and obese body weight.

RESULTS

The semaglutide PBPK model was successfully developed in adults and scaled to the paediatric population. Our paediatric PBPK simulations indicated a significant increase in maximum plasma concentrations for the 10-14 years' paediatric population with healthy body weights, which was higher than the observed values in adults at the reference dose. Since gastrointestinal adverse events are related to increased semaglutide concentrations, peak concentrations outside the target range may represent a safety risk for this paediatric age group. Besides, paediatric PBPK models indicated that body weight was inversely related to semaglutide maximum plasma concentration, corroborating the consensus on the influence of body weight on semaglutide PK in adults.

CONCLUSION

Paediatric PBPK was successfully achieved using a top-down approach and drug-related parameters. The development of unprecedented PBPK models will support paediatric clinical therapy for applying aid-safe dosing regimens for the paediatric population in diabetes treatment.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

Revamping Biopharmaceutics-Pharmacokinetics with Scientific and Regulatory Implications for Oral Drug Absorption

PURPOSE

The Wagner-Nelson and Loo-Riegelman methods developed in the 1960s and used since for the construction of percent of drug absorbed as a function of time as well as in in vitro in vivo correlations are re-considered in the light of the physiologically sound Finite Absorption Time (F.A.T.) concept developed recently.

METHODS

The classical equations for the percentage of drug absorption as a function of time were modified by taking into account the termination of drug absorption at F.A.T., replacing the parameters associated with the assumption of infinite drug absorption.

RESULTS

Mathematical analysis using the relevant Physiologically Based Pharmacokinetic Finite Time (PBFTK) models assuming one- or two-compartment drug disposition, revealed that the modified %absorbed versus time curves are of bilinear type with an ascending limb intersecting the horizontal line at F.A.T. A computer-based methodology is described for the estimation of F.A.T. from experimental data. More than one linear ascending limb is found when more than one absorption phase is operating. Experimental data were analyzed and the estimates for F.A.T were found to be similar to those derived from nonlinear regression analysis using PBFTPK models.

CONCLUSION

These results place an end to the routinely reported exponential %absorbed versus time curves prevailing in biopharmaceutics-pharmacokinetics since their inception in the'60 s. These findings point to the use of the F.A.T. concept in drug absorption research and regulatory guidelines such as deconvolution techniques for the assessment of drug input rate, stochastic mean absorption time calculations, population analyses, in vitro in vivo correlations and bioequivalence guidelines.

Investigating Tacrolimus Disposition in Paediatric Patients with a Physiologically Based Pharmacokinetic Model Incorporating CYP3A4 Ontogeny, Mechanistic Absorption and Red Blood Cell Binding

Tacrolimus is a crucial immunosuppressant for organ transplant patients, requiring therapeutic drug monitoring due to its variable exposure after oral intake. Physiologically based pharmacokinetic (PBPK) modelling has provided insights into tacrolimus disposition in adults but has limited application in paediatrics. This study investigated age dependency in tacrolimus exposure at the levels of absorption, metabolism, and distribution. Based on the literature data, a PBPK model was developed to predict tacrolimus exposure in adults after intravenous and oral administration. This model was then extrapolated to the paediatric population, using a unique reference dataset of kidney transplant patients. Selecting adequate ontogeny profiles for hepatic and intestinal CYP3A4 appeared critical to using the model in children. The best model performance was achieved by using the Upreti ontogeny in both the liver and intestines. To mechanistically evaluate the impact of absorption on tacrolimus exposure, biorelevant in vitro solubility and dissolution data were obtained. A relatively fast and complete release of tacrolimus from its amorphous formulation was observed when mimicking adult or paediatric dissolution conditions (dose, fluid volume). In both the adult and paediatric PBPK models, the in vitro dissolution profiles could be adequately substituted by diffusion-layer-based dissolution modelling. At the level of distribution, sensitivity analysis suggested that differences in blood plasma partitioning of tacrolimus may contribute to the variability in exposure in paediatric patients.

Physiologically Based Pharmacokinetic Modeling of Extracellular Vesicles

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.

Physiologically based pharmacokinetic model to predict drug-drug interactions with the antibody-drug conjugate enfortumab vedotin

Enfortumab vedotin is an antibody-drug conjugate (ADC) comprised of a Nectin-4-directed antibody and monomethyl auristatin E (MMAE), which is primarily eliminated through P-glycoprotein (P-gp)-mediated excretion and cytochrome P450 3A4 (CYP3A4)-mediated metabolism. A physiologically based pharmacokinetic (PBPK) model was developed to predict effects of combined P-gp with CYP3A4 inhibitor/inducer (ketoconazole/rifampin) on MMAE exposure when coadministered with enfortumab vedotin and study enfortumab vedotin with CYP3A4 (midazolam) and P-gp (digoxin) substrate exposure. A PBPK model was built for enfortumab vedotin and unconjugated MMAE using the PBPK simulator ADC module. A similar model was developed with brentuximab vedotin, an ADC with the same valine-citrulline-MMAE linker as enfortumab vedotin, for MMAE drug-drug interaction (DDI) verification using clinical data. The DDI simulation predicted a less-than-2-fold increase in MMAE exposure with enfortumab vedotin plus ketoconazole (MMAE geometric mean ratio [GMR] for maximum concentration [Cmax], 1.15; GMR for area under the time-concentration curve from time 0 to last quantifiable concentration [AUClast], 1.38). Decreased MMAE exposure above 50% but below 80% was observed with enfortumab vedotin plus rifampin (MMAE GMR Cmax, 0.72; GMR AUClast, 0.47). No effect of enfortumab vedotin on midazolam or digoxin systemic exposure was predicted. Results suggest that combination enfortumab vedotin, P-gp, and a CYP3A4 inhibitor may result in increased MMAE exposure and patients should be monitored for potential adverse effects. Combination P-gp and a CYP3A4 inducer may result in decreased MMAE exposure. No exposure change is expected for CYP3A4 or P-gp substrates when combined with enfortumab vedotin.ClinicalTrials.gov identifier Not applicable.

Progressive tools and critical strategies for development of best fit PBPK model aiming better in vitro-in vivo correlation

Nowadays, conducting discriminative dissolution experiments employing physiologically based pharmacokinetic modeling (PBPK) or physiologically based biopharmaceutical modeling (PBBM) is gaining significant importance in quantitatively predicting oral absorption of drugs. Mechanistic understanding of each process involved in drug absorption and its impact on the performance greatly facilitates designing a formulation with high confidence. Unfortunately, the biggest challenge scientists are facing in current days is the lack of standardized protocol for integrating dissolution experiment data during PBPK modeling. However, in vitro-in vivo drug release interrelation can be improved with the consideration and development of appropriate biorelevant dissolution media that closely mimic physiological conditions. Multiple reported dissolution models have described nature and functionality of different regions of the gastrointestinal tract (GI) to more accurately design discriminative dissolution media. Dissolution experiment data can be integrated either mechanistically or without a mechanism depending primarily on the formulation type, biopharmaceutics classification system (BCS) class and particle size of the drug substance. All such parameters are required to be considered for selecting the appropriate functions during PBPK modeling to produce a best fit model. The primary focus of this review is to critically discuss various progressive dissolution models and tools, existing challenges and approaches for establishing best fit PBPK model aiming better in vitro-in vivo correlation (IVIVC). Strategies for proper selection of dissolution models as an input function in PBPK/PBBM modeling have also been critically discussed. Logical and scientific pathway for selection of different type of functions and integration events in the commercially available in silico software has been described through case studies.

Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations

The evolution in the development of drugs has increased the popularity of physiologically based pharmacokinetic (PBPK) models. This study seeks to assess the PK of metoprolol in populations with healthy, chronic kidney disease (CKD), and acute myocardial infarction (AMI) conditions by developing and evaluating PBPK models. An extensive literature review for identifying and selecting plasma concentration vs time profile data and other drug-related parameters was undergone for their integration into the PK-Sim program followed by the development of intravenous, oral, and diseased models. The developed PBPK model of metoprolol was then evaluated using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for all PK parameters, i.e., the area under the curve (AUC), maximal plasma concentration, and clearance. The model evaluation depicted that none of the PK parameters were out of the allowed range (2-fold error) in the case of the mean Robs/pre ratios. The model anticipations were executed to determine the influence of diseases on unbound and total AUC after the application of metoprolol in healthy, moderate, and severe CKD. The dosage reductions were also suggested based on differences in unbound and total AUC in different stages of CKD. The developed PBPK models have successfully elaborated the PK changes of metoprolol occurring in healthy individuals and those with renal and heart diseases (CKD & AMI), which may be fruitful for dose optimization among diseased patients.

The In Vitro, In Vivo, and PBPK Evaluation of a Novel Lung-Targeted Cardiac-Safe Hydroxychloroquine Inhalation Aerogel

Hydroxychloroquine (HCQ) was repurposed for COVID-19 treatment. Subtherapeutic HCQ lung levels and cardiac toxicity of oral HCQ were overcome by intratracheal (IT) administration of lower HCQ doses. The crosslinker-free supercritical fluid technology (SFT) produces aerogels and impregnates them with drugs in their amorphous form with efficient controlled release. Mechanistic physiologically based pharmacokinetic (PBPK) modeling can predict the lung's epithelial lining fluid (ELF) drug levels. This study aimed to develop a novel HCQ SFT formulation for IT administration to achieve maximal ELF levels and minimal cardiac toxicity. HCQ SFT formulation was prepared and evaluated for physicochemical, in vitro release, pharmacokinetics, and cardiac toxicity. Finally, the rat HCQ ELF concentrations were predicted using PBPK modeling. HCQ was amorphous after loading into the chitosan-alginate nanoporous microparticles (22.7±7.6 μm). The formulation showed a zero-order release, with only 40% released over 30 min compared to 94% for raw HCQ. The formulation had a tapped density of 0.28 g/cm3 and a loading efficiency of 35.3±1.3%. The IT administration of SFT HCQ at 1 mg/kg resulted in 23.7-fold higher bioavailability, fourfold longer MRT, and eightfold faster absorption but lower CK-MB and LDH levels than oral raw HCQ at 4 mg/kg. The PBPK model predicted 6 h of therapeutic ELF levels for IT SFT HCQ and a 100-fold higher ELF-to-heart concentration ratio than oral HCQ. Our findings support the feasibility of lung-targeted and more effective SFT HCQ IT administration for COVID-19 compared to oral HCQ with less cardiac toxicity. Graphical abstract.

Revisiting the in-vitro and in-vivo considerations for in-silico modelling of complex injectable drug products

Complex injectable drug products (CIDPs) have often been developed to modulate the pharmacokinetics along with efficacy for therapeutic agents used for remediation of chronic disorders. The effective development of CIDPs has exhibited complex kinetics associated with multiphasic drug release from the prepared formulations. Consequently, predictability of pharmacokinetic modelling for such CIDPs has been difficult and there is need for advanced complex computational models for the establishment of accurate prediction models for in-vitro-in-vivo correlation (IVIVC). The computational modelling aims at supplementing the existing knowledge with mathematical equations to develop formulation strategies for generation of predictable and discriminatory IVIVC. Such an approach would help in reduction of the burden of effect of hidden factors on preclinical to clinical translations. Computational tools like physiologically based pharmacokinetics (PBPK) modelling have combined physicochemical and physiological properties along with IVIVC characteristics of clinically used formulations. Such techniques have helped in prediction and understanding of variability in pharmacodynamic parameters of potential generic products to clinically used formulations like Doxil®, Ambisome®, Abraxane® in healthy and diseased population using mathematical equations. The current review highlights the important formulation characteristics, in-vitro, preclinical in-vivo aspects which need to be considered while developing a stimulatory predictive PBPK model in establishment of an IVIVC and in-vitro-in-vivo relationship (IVIVR).

Interethnic differences in drug response: projected impact of genetic variations in the Saudi population

Ethnicity is known to have an impact on drug responses. This is particularly important for drugs that have a narrow therapeutic window, nonlinearity in pharmacokinetics and are metabolized by enzymes that demonstrate genetic polymorphisms. However, most clinical trials are conducted among Caucasians, which might limit the usefulness of the findings of such studies for other ethnicities. The representation of participants from Saudi Arabia in global clinical trials is low. Therefore, there is a paucity of evidence to assess the impact of ethnic variability in the Saudi population on drug response. In this article, the authors assess the projected impact of genetic polymorphisms in drug-metabolizing enzymes and drug targets on drug response in the Saudi population.

Physiologically based pharmacokinetic modeling of levetiracetam to predict the exposure in hepatic and renal impairment and elderly populations

Levetiracetam (LEV) is an anti-epileptic drug approved for use in various populations. The pharmacokinetic (PK) behavior of LEV may be altered in the elderly and patients with renal and hepatic impairment. Thus, dosage adjustment is required. This study was conducted to investigate how the physiologically-based PK (PBPK) model describes the PKs of LEV in adult and elderly populations, as well as to predict the PKs of LEV in patients with renal and hepatic impairment in both populations. The whole-body PBPK models were developed using the reported physicochemical properties of LEV and clinical data. The models were validated using data from clinical studies with different dose ranges and different routes and intervals of administration. The fit performance of the models was assessed by comparing predicted and observed blood concentration data and PK parameters. It is recommended that the doses be reduced to ~70%, 60%, and 45% of the adult dose for the mild, moderate, and severe renal impairment populations and ~95%, 80%, and 57% of the adult dose for the Child Pugh-A (CP-A), Child Pugh-B (CP-B), and Child Pugh-C (CP-C) hepatic impairment populations, respectively. No dose adjustment is required for the healthy elderly population, but dose reduction is required for the elderly with organ dysfunction accordingly, on a scale similar to that of adults. A PBPK model of LEV was successfully developed to optimize dosing regimens for special populations.

Modelling Based Approaches to Support Generic Drug Regulatory Submissions-Practical Considerations and Case Studies

Model informed drug development (MiDD) is useful to predict in vivo exposure of drugs during various stages of the drug development process. This approach employs a variety of quantitative tools to assess the risks during the drug development process. One important tool in the MiDD tool kit is the Physiologically Based Pharmacokinetic Modelling (PBPK). This tool is extensively used to reduce the development cost and to accelerate the access of medicines to the patients. In this work, we provide an overview of PBPK modelling approaches in the generic drug development process, with a special emphasis on the bio-waiver applications. We describe herein approaches and common pitfalls while submitting model based justifications as a response to the regulatory deficiencies during the generic drug development process. With some in-house case studies, we have attempted to provide a clear path for PBPK model based justifications for bio-waivers. With this review, the gap between theoretical knowledge and practical application of modelling and simulation tools for generic drug product development could be potentially reduced.

Mechanistic evaluation of the inhibitory effect of four SGLT-2 inhibitors on SGLT 1 and SGLT 2 using physiologically based pharmacokinetic (PBPK) modeling approaches

Sodium-glucose co-transporter type 2 (SGLT 2, gliflozins) inhibitors are potent orally active drugs approved for managing type 2 diabetes. SGLT 2 inhibitors exert a glucose-lowering effect by suppressing sodium-glucose co-transporters 1 and 2 in the intestinal and kidney proximal tubules. In this study, we developed a physiologically based pharmacokinetic (PBPK) model and simulated the concentrations of ertugliflozin, empagliflozin, henagliflozin, and sotagliflozin in target tissues. We used the perfusion-limited model to illustrate the disposition of SGLT 2 inhibitors in vivo. The modeling parameters were obtained from the references. Simulated steady-state plasma concentration-time curves of the ertugliflozin, empagliflozin, henagliflozin, and sotagliflozin are similar to the clinically observed curves. The 90% prediction interval of simulated excretion of drugs in urine captured the observed data well. Furthermore, all corresponding model-predicted pharmacokinetic parameters fell within a 2-fold prediction error. At the approved doses, we estimated the effective concentrations in intestinal and kidney proximal tubules and calculated the inhibition ratio of SGLT transporters to differentiate the relative inhibition capacities of SGLT1 and 2 in each gliflozin. According to simulation results, four SGLT 2 inhibitors can nearly completely inhibit SGLT 2 transporter at the approved dosages. Sotagliflozin exhibited the highest inhibition activity on SGLT1, followed by ertugliflozin, empagliflozin, and henagliflozin, which showed a lower SGLT 1 inhibitory effect. The PBPK model successfully simulates the specific target tissue concentration that cannot be measured directly and quantifies the relative contribution toward SGLT 1 and 2 for each gliflozin.

The MPRINT Hub Data, Model, Knowledge and Research Coordination Center: Bridging the gap in maternal-pediatric therapeutics research through data integration and pharmacometrics

Maternal and pediatric populations have historically been considered "therapeutic orphans" due to their limited inclusion in clinical trials. Physiologic changes during pregnancy and lactation and growth and maturation of children alter pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. Precision therapy in these populations requires knowledge of these effects. Efforts to enhance maternal and pediatric participation in clinical studies have increased over the past few decades. However, studies supporting precision therapeutics in these populations are often small and, in isolation, may have limited impact. Integration of data from various studies, for example through physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or bioinformatics approaches, can augment the value of data from these studies, and help identify gaps in understanding. To catalyze research in maternal and pediatric precision therapeutics, the Obstetric and Pediatric Pharmacology and Therapeutics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub. Herein, we provide an overview of the status of maternal-pediatric therapeutics research and introduce the Indiana University-Ohio State University MPRINT Hub Data, Model, Knowledge and Research Coordination Center (DMKRCC), which aims to facilitate research in maternal and pediatric precision therapeutics through the integration and assessment of existing knowledge, supporting pharmacometrics and clinical trials design, development of new real-world evidence resources, educational initiatives, and building collaborations among public and private partners, including other NICHD-funded networks. By fostering use of existing data and resources, the DMKRCC will identify critical gaps in knowledge and support efforts to overcome these gaps to enhance maternal-pediatric precision therapeutics.

In-depth analysis of patterns in selection of different physiologically based pharmacokinetic modeling tools: PartI - Applications and rationale behind the use of open source-code software

PBPK applications published in the literature support a greater adoption of non-open source-code (NOSC) software as opposed to open source-code (OSC) alternatives. However, a significant number of PBPK modelers are still using OSC software, understanding the rationale for the use of this modality is important and may help those embarking on PBPK modeling. No previous analysis of PBPK modeling trends has included the rationale of the modeler. An in-depth analysis of PBPK applications of OSC software is warranted to determine the true impact of OSC software on the rise of PBPK. Publications focussing on PBPK modeling applications, which used OSC software, were identified by systematically searching the scientific literature for original articles. A total of 171 articles were extracted from the narrowed subset. The rise in the use of OSC software for PBPK applications was greater than the general discipline of pharmacokinetics (9 vs. 4), but less than the overall growth of the PBPK area (9 vs. 43). Our report demonstrates conclusively that the surge in PBPK usage is primarily attributable to the availability and implementations of NOSC software. Modelers preferred not to share the reasons for their selection of certain modeling software and no 'explicit' rationale was given to support the use of OSC analysed by this study. As the preference for NOSC versus OSC software tools in the PBPK area continues to be divided, initiatives to add the rationale in using one form over another to every future PBPK modeling report will be a welcomed and informative addition.

Physiologically-Based Pharmacokinetic Modelling to Predict the Pharmacokinetics and Pharmacodynamics of Linezolid in Adults and Children with Tuberculous Meningitis

Linezolid is used off-label for treatment of central nervous system infections. However, its pharmacokinetics and target attainment in cranial cerebrospinal fluid (CSF) in tuberculous meningitis patients is unknown. This study aimed to predict linezolid cranial CSF concentrations and assess attainment of pharmacodynamic (PD) thresholds (AUC:MIC of >119) in plasma and cranial CSF of adults and children with tuberculous meningitis. A physiologically based pharmacokinetic (PBPK) model was developed to predict linezolid cranial CSF profiles based on reported plasma concentrations. Simulated steady-state PK curves in plasma and cranial CSF after linezolid doses of 300 mg BID, 600 mg BID, and 1200 mg QD in adults resulted in geometric mean AUC:MIC ratios in plasma of 118, 281, and 262 and mean cranial CSF AUC:MIC ratios of 74, 181, and 166, respectively. In children using ~10 mg/kg BID linezolid, AUC:MIC values at steady-state in plasma and cranial CSF were 202 and 135, respectively. Our model predicts that 1200 mg per day in adults, either 600 mg BID or 1200 mg QD, results in reasonable (87%) target attainment in cranial CSF. Target attainment in our simulated paediatric population was moderate (56% in cranial CSF). Our PBPK model can support linezolid dose optimization efforts by simulating target attainment close to the site of TBM disease.

Artificial intelligence for compound pharmacokinetics prediction

Optimisation of compound pharmacokinetics (PK) is an integral part of drug discovery and development. Animal in vivo PK data as well as human and animal in vitro systems are routinely utilised to evaluate PK in humans. In recent years machine learning and artificial intelligence (AI) emerged as a major tool for modelling of in vivo animal and human PK, enabling prediction from chemical structure early in drug discovery, and therefore offering opportunities to guide the design and prioritisation of molecules based on relevant in vivo properties and, ultimately, predicting human PK at the point of design. This review presents recent advances in machine learning and AI models for in vivo animal and human PK for small-molecule compounds as well as some examples for antibody therapeutics.

Use of In Vitro Dynamic Colon Model (DCM) to Inform a Physiologically Based Biopharmaceutic Model (PBBM) to Predict the In Vivo Performance of a Modified-Release Formulation of Theophylline

A physiologically based biopharmaceutic model (PBBM) of a modified-release formulation of theophylline (Uniphyllin Continus® 200 mg tablet) was developed and implemented to predict the pharmacokinetic (PK) data of healthy male volunteers by integrating dissolution profiles measured in a biorelevant in vitro model: the Dynamic Colon Model (DCM). The superiority of the DCM over the United States Pharmacopeia (USP) Apparatus II (USP II) was demonstrated by the superior predictions for the 200 mg tablet (average absolute fold error (AAFE): 1.1-1.3 (DCM) vs. 1.3-1.5 (USP II). The best predictions were obtained using the three motility patterns (antegrade and retrograde propagating waves, baseline) in the DCM, which produced similar PK profiles. However, extensive erosion of the tablet occurred at all agitation speeds used in USP II (25, 50 and 100 rpm), resulting in an increased drug release rate in vitro and overpredicted PK data. The PK data of the Uniphyllin Continus® 400 mg tablet could not be predicted with the same accuracy using dissolution profiles from the DCM, which might be explained by differences in upper gastrointestinal (GI) tract residence times between the 200 and 400 mg tablets. Thus, it is recommended that the DCM be used for dosage forms in which the main release phenomena take place in the distal GI tract. However, the DCM again showed a better performance based on the overall AAFE compared to the USP II. Regional dissolution profiles within the DCM cannot currently be integrated into Simcyp®, which might limit the predictivity of the DCM. Thus, further compartmentalization of the colon within PBBM platforms is required to account for observed intra-regional differences in drug distribution.

Gene expression databases for physiologically based pharmacokinetic modeling of humans and animal species

In drug research, developing a sound understanding of the key mechanistic drivers of pharmacokinetics (PK) for new molecular entities is essential for human PK and dose predictions. Here, characterizing the absorption, distribution, metabolism, and excretion (ADME) processes is crucial for a mechanistic understanding of the drug-target and drug-body interactions. Sufficient knowledge on ADME processes enables reliable interspecies and human PK estimations beyond allometric scaling. The physiologically based PK (PBPK) modeling framework allows the explicit consideration of organ-specific ADME processes. The sum of all passive and active ADME processes results in the observed plasma PK. Gene expression information can be used as surrogate for protein abundance and activity within PBPK models. The absolute and relative expression of ADME genes can differ between species and strains. This is affecting both, the PK and pharmacodynamics and is therefore posing a challenge for the extrapolation from preclinical findings to humans. We developed an automated workflow that generates whole-body gene expression databases for humans and other species relevant in drug development, animal health, nutritional sciences, and toxicology. Solely, bulk RNA-seq data curated and provided by the Swiss Institute of Bioinformatics from healthy, normal, and untreated primary tissue samples were considered as an unbiased reference of normal gene expression. The databases are interoperable with the Open Systems Pharmacology Suite (PK-Sim and MoBi) and enable seamless access to a central source of curated cross-species gene expression data. This will increase data transparency, increase reliability and reproducibility of PBPK model simulations, and accelerate mechanistic PBPK model development in the future.

Investigation of simplified physiologically-based pharmacokinetic models in rat and human

Whole-body physiologically-based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug-dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug-independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.

The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Physiologically based Pharmacokinetic Models under the Prism of the Finite Absorption Time Concept

To date, mechanistic modeling of oral drug absorption has been achieved via the use of physiologically based pharmacokinetic (PBPK) modeling, and more specifically, physiologically based biopharmaceutics model (PBBM). The concept of finite absorption time (FAT) has been developed recently and the application of the relevant physiologically based finite time pharmacokinetic (PBFTPK) models to experimental data provides explicit evidence that drug absorption terminates at a specific time point. In this manuscript, we explored how PBBM and PBFTPK models compare when applied to the same dataset. A set of six compounds with clinical data from immediate-release formulation were selected. Both models resulted in absorption time estimates within the small intestinal transit time, with PBFTPK models generally providing shorter time estimates. A clear relationship between the absorption rate and the product of permeability and luminal concentration was observed, in concurrence with the fundamental assumptions of PBFTPK models. We propose that future research on the synergy between the two modeling approaches can lead to both improvements in the initial parameterization of PBPK/PBBM models but to also expand mechanistic oral absorption concepts to more traditional pharmacometrics applications.

Physiologically-based toxicokinetic model for the prediction of perchlorate distribution and its application

Perchlorate is a stable and readily transportable thyroid hormone disruptor, and prevalent exposure to perchlorate through food and drinking water has raised public concern about its health effects. The physiologically based toxicokinetic (PBTK) model as a dose prediction method is effective to predict the toxicant exposure dose of an organism and helps quantitatively assess the dose-dependent relationship with toxic effects. The current study aimed to establish a multi-compartment PBTK model based on updated time-course datasets of single oral exposure to perchlorate in rats. With adjustment of the kinetic parameters, the model fitted well the toxicokinetic characteristics of perchlorate in urine, blood, and thyroid from our experiments and the literature, and the coefficient of determination (R²) between the fitting values and the experimental data in regression analysis was greater than 0.91, indicating the robustness of the current model. The results of sensitivity analysis and daily repeated exposure simulations together confirmed its effective renal clearance. According to the distribution characteristic of perchlorate, a correlation study of internal and external exposure was conducted using urinary perchlorate as a bioassay indicator. The developed multi-compartment model for perchlorate updates important toxicokinetic data and kinetic parameters, providing analytical and modeling tools for deriving total exposure levels in the short term.

Clinical Ocular Exposure Extrapolation for Ophthalmic Solutions Using PBPK Modeling and Simulation

BACKGROUND

The development of generic ophthalmic drug products is challenging due to the complexity of the ocular system, and a lack of sensitive testing to evaluate the interplay of physiology with ophthalmic formulations. While measurements of drug concentration at the site of action in humans are typically sparse, these measurements are more easily obtained in rabbits. The purpose of this study is to demonstrate the utility of an ocular physiologically based pharmacokinetic (PBPK) model for translation of ocular exposure from rabbit to human.

METHOD

The Ocular Compartmental Absorption and Transit (OCAT™) model within GastroPlus® v9.8.2 was used to build PBPK models for levofloxacin (Lev), moxifloxacin (Mox), and gatifloxacin (Gat) ophthalmic solutions. in the rabbit eye. The models were subsequently used to predict Lev, Mox, and Gat exposure after ocular solution administrations in humans. Drug-specific parameters were used as fitted and validated in the rabbit OCAT model. The physiological parameters were scaled to match human ocular physiology.

RESULTS

OCAT model simulations for rabbit well described the observed concentrations in the eye compartments following Lev, Mox, and Gat solution administrations of different doses and various administration schedules. The clinical ocular exposure following ocular administration of Lev, Mox, and Gat solutions at different doses and various administration schedules was well predicted.

CONCLUSION

Even though additional case studies for different types of active pharmaceutical ingredients (APIs) and formulations will be needed, the current study represents an important step in the validation of the extrapolation method to predict human ocular exposure for ophthalmic drug products using PBPK models.

Clinical significance of the rivaroxaban-dronedarone interaction: insights from physiologically based pharmacokinetic modelling

Patients with atrial fibrillation may require rhythm control therapy in addition to anticoagulation therapy for the prevention of stroke. Since 2012, the European Society of Cardiology and European Heart Rhythm Association guidelines have recommended non-vitamin K antagonist oral anticoagulants, including rivaroxaban, for the prevention of stroke in patients with atrial fibrillation. During the same period, these guidelines have also recommended dronedarone or amiodarone as second-line rhythm control agents in certain patients with atrial fibrillation and no contraindications. Amiodarone and dronedarone both strongly inhibit P-glycoprotein, while dronedarone is a moderate and amiodarone a weak inhibitor of cytochrome P450 3A4 (CYP3A4). Based on these data and evidence from physiologically based pharmacokinetic modelling, amiodarone and dronedarone are expected to have similar effects on rivaroxaban exposure resulting from P-glycoprotein and CYP3A4 inhibition. However, the rivaroxaban label recommends against the concomitant use of dronedarone, but not amiodarone, citing a lack of evidence on the concomitant use of rivaroxaban and dronedarone as the reason for the different recommendations. In this report, we discuss evidence from clinical studies and physiologically based pharmacokinetic modelling on the potential for increased rivaroxaban exposure resulting from drug-drug interaction between rivaroxaban and dronedarone or amiodarone. The current evidence supports the same clinical status and concomitant use of either amiodarone or dronedarone with rivaroxaban, which could be considered in future recommendations.

A proof of concept reinforcement learning based tool for non parametric population pharmacokinetics workflow optimization

The building of population pharmacokinetic models can be described as an iterative process in which given a model and a dataset, the pharmacometrician introduces some changes to the model specification, then perform an evaluation and based on the predictions obtained performs further optimization. This process (perform an action, witness a result, optimize your knowledge) is a perfect scenario for the implementation of Reinforcement Learning algorithms. In this paper we present the conceptual background and a implementation of one of those algorithms aiming to show pharmacometricians how to automate (to a certain point) the iterative model building process.We present the selected discretization for the action and the state space. SARSA (State-Action-Reward-State-Action) was selected as the RL algorithm to use, configured with a window of 1000 episodes with and a limit of 30 actions per episode. SARSA was configured to control an interface to the Non-Parametric Optimal Design algorithm, that was actually performing the parameter optimization.The Reinforcement Learning (RL) based agent managed to obtain the same likelihood and number of support points, with a distribution similar to the reported in the original paper. The total amount of time used by the train the agent was 5.5 h although we think this time can be further improved. It is possible to automatically find the structural model that maximizes the final likelihood for an specific pharmacokinetic dataset by using RL algorithm. The framework provided could allow the integration of even more actions i.e: add/remove covariates, non-linear compartments or the execution of secondary analysis. Many limitations were found while performing this study but we hope to address them all in future studies.

Prediction of total and renal clearance of renally secreted drugs in neonates and infants (≤3 months of age)

BACKGROUND

Renal excretion is a major route of elimination for many drugs. Renal clearance is the sum of three processes: glomerular filtration, tubular secretion, and tubular re-absorption. Tubular secretion is an active transport process and is immature at birth. In the neonates, renal tubular secretion can be important for the elimination of those drugs which are renally secreted, such as penicillins and cephalosporins.

AIM

The objective of this study was to evaluate the predictive performances of three models to predict total and renal clearance of renally secreted drugs in neonates (≤3 months of age).

METHODS

From the literature, clearance values for 12 renally secreted drugs for neonates and adults were obtained. Three models were used to predict the clearances of these drugs. The predictive performances of these models were evaluated by comparing the predicted values of total and renal clearance with the observed clearance values in the neonates.

RESULTS

There were 12 drugs with 22 observations (preterm and term neonates, ≤3 months of age) for total clearance and six drugs with eight observations for renal clearance. For both total and renal clearance, a prediction error of <50% was observed by all three models evaluated in this study.

CONCLUSIONS

The proposed models can predict mean total and renal clearances of renally secreted drugs in preterm and term neonates (≤3 months of age) with reasonable accuracy (50% prediction error) and are of practical value during neonatal drug development.

RELEVANCE FOR PATIENTS

The work may help in dose selection for neonates for medicines that are renally secreted.

In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools

In the past decade, only a small number of papers have elaborated on the application of physiologically based pharmacokinetic (PBPK) modeling across different areas. In this review, an in-depth analysis of the distribution of PBPK modeling in relation to its application in various research topics and model validation was conducted by text mining tools. Orange 3.32.0, an open-source data mining program was used for text mining. PubMed was used for data retrieval, and the collected articles were analyzed by several widgets. A total of 2699 articles related to PBPK modeling met the predefined criteria. The number of publications per year has been rising steadily. Regarding the application areas, the results revealed that 26% of the publications described the use of PBPK modeling in early drug development, risk assessment and toxicity assessment, followed by absorption/formulation modeling (25%), prediction of drug-disease interactions (20%), drug-drug interactions (DDIs) (17%) and pediatric drug development (12%). Furthermore, the analysis showed that only 12% of the publications mentioned model validation, of which 51% referred to literature-based validation and 26% to experimentally validated models. The obtained results present a valuable review of the state-of-the-art regarding PBPK modeling applications in drug discovery and development and related fields.

Possible Mechanisms of the Interplay between Drugs and Mycotoxins-Is There a Possible Impact?

Mycotoxin contamination is a global food safety issue leading to major public health concerns. Repeated exposure to multiple mycotoxins not only has repercussions on human health but could theoretically also lead to interactions with other xenobiotic substances-such as drugs-in the body by altering their pharmacokinetics and/or pharmacodynamics. The combined effects of chronic drug use and mycotoxin exposure need to be well understood in order to draw valid conclusions and, in due course, to develop guidelines. The aim of this review is to focus on food contaminants, more precisely on mycotoxins, and drugs. First, a description of relevant mycotoxins and their effects on human health and metabolism is presented. The potential for interactions of mycotoxins with drugs using in vitro and in vivo animal experiments is summarized. Predictive software tools for unraveling mycotoxin-drug interactions are proposed and future perspectives on this emerging topic are highlighted with a view to evaluate associated risks and to focus on precision medicine. In vitro and in vivo animal studies have shown that mycotoxins affect CYP450 enzyme activity. An impact from drugs on mycotoxins mediated via CYP450-enzymes is plausible; however, an impact of mycotoxins on drugs is less likely considering the much smaller dose exposure to mycotoxins. Drugs that are CYP450 perpetrators and/or substrates potentially influence the metabolism of mycotoxins, metabolized via these CYP450 enzymes. To date, very little research has been conducted on this matter. The only statistically sound reports describe mycotoxins as victims and drugs as perpetrators in interactions; however, more analysis on mycotoxin-drug interactions needs to be performed.

Feasibility of a Pragmatic PBPK Modeling Approach: Towards Model-Informed Dosing in Pediatric Clinical Care

BACKGROUND AND OBJECTIVE

More than half of all drugs are still prescribed off-label to children. Pharmacokinetic (PK) data are needed to support off-label dosing, however for many drugs such data are either sparse or not representative. Physiologically-based pharmacokinetic (PBPK) models are increasingly used to study PK and guide dosing decisions. Building compound models to study PK requires expertise and is time-consuming. Therefore, in this paper, we studied the feasibility of predicting pediatric exposure by pragmatically combining existing compound models, developed e.g. for studies in adults, with a pediatric and preterm physiology model.

METHODS

Seven drugs, with various PK characteristics, were selected (meropenem, ceftazidime, azithromycin, propofol, midazolam, lorazepam, and caffeine) as a proof of concept. Simcyp® v20 was used to predict exposure in adults, children, and (pre)term neonates, by combining an existing compound model with relevant virtual physiology models. Predictive performance was evaluated by calculating the ratios of predicted-to-observed PK parameter values (0.5- to 2-fold acceptance range) and by visual predictive checks with prediction error values.

RESULTS

Overall, model predicted PK in infants, children and adolescents capture clinical data. Confidence in PBPK model performance was therefore considered high. Predictive performance tends to decrease when predicting PK in the (pre)term neonatal population.

CONCLUSION

Pragmatic PBPK modeling in pediatrics, based on compound models verified with adult data, is feasible. A thorough understanding of the model assumptions and limitations is required, before model-informed doses can be recommended for clinical use.

Individualized Patient Care Through Model-Informed Precision Dosing: Reflections on Training Future Practitioners

Prior to his passing, Dr. Roger Jelliffe, expressed the need for educating future physicians and clinical pharmacists on the availability of computer-based tools to support dose optimization in patients in stable or unstable physiological states. His perspectives were to be captured in a commentary for the AAPS J with a focus on incorporating population pharmacokinetic (PK)/pharmacodynamic (PD) models that are designed to hit the therapeutic target with maximal precision. Unfortunately, knowing that he would be unable to complete this project, Dr. Jelliffe requested that a manuscript conveying his concerns be completed upon his passing. With this in mind, this final installment of the AAPS J theme issue titled "Alternative Perspectives for Evaluating Drug Exposure Characteristics in a Population - Avoiding Analysis Pitfalls and Pigeonholes" is an effort to honor Dr. Jelliffe's request, conveying his concerns and the need to incorporate modeling and simulation into the training of physicians and clinical pharmacists. Accordingly, Dr. Jelliffe's perspectives have been integrated with those of the other three co-authors on the following topics: the clinical utility of population PK models; the role of multiple model (MM) dosage regimens to identify an optimal dose for an individual; tools for determining dosing regimens in renal dialysis patients (or undergoing other therapies that modulate renal clearance); methods to analyze and track drug PK in acutely ill patients presenting with high inter-occasion variability; implementation of a 2-cycle approach to minimize the duration between blood samples taken to estimate the changing PK in an acutely ill patient and for the generation of therapeutic decisions in advance for each dosing cycle based on an analysis of the previous cycle; and the importance of expressing therapeutic drug monitoring results as 1/variance rather than as the coefficient of variation. Examples showcase why, irrespective of the overall approach, the combination of therapeutic drug monitoring and computer-informed precision dosing is indispensable for maximizing the likelihood of achieving the target drug concentrations in the individual patient.

Organ-On-A-Chip Database Revealed-Achieving the Human Avatar in Silicon

Organ-on-a-chip (OOC) provides microphysiological conditions on a microfluidic chip, which makes up for the shortcomings of traditional in vitro cellular culture models and animal models. It has broad application prospects in drug development and screening, toxicological mechanism research, and precision medicine. A large amount of data could be generated through its applications, including image data, measurement data from sensors, ~omics data, etc. A database with proper architecture is required to help scholars in this field design experiments, organize inputted data, perform analysis, and promote the future development of novel OOC systems. In this review, we overview existing OOC databases that have been developed, including the BioSystics Analytics Platform (BAP) developed by the University of Pittsburgh, which supports study design as well as data uploading, storage, visualization, analysis, etc., and the organ-on-a-chip database (Ocdb) developed by Southeast University, which has collected a large amount of literature and patents as well as relevant toxicological and pharmaceutical data and provides other major functions. We used examples to overview how the BAP database has contributed to the development and applications of OOC technology in the United States for the MPS consortium and how the Ocdb has supported researchers in the Chinese Organoid and Organs-On-A-Chip society. Lastly, the characteristics, advantages, and limitations of these two databases were discussed.

Physiologically Based Pharmacokinetic (PBPK) Modeling of Lornoxicam: Exploration of doses for CYP2C9 Genotypes and Patients with Cirrhosis

Lornoxicam physiologically based pharmacokinetic (PBPK) models were developed and validated on the basis of clinical pharmacokinetic results obtained by considering CYP2C9 genetic polymorphisms in healthy adult populations. PBPK models were extended to predict lornoxicam pharmacokinetics for patients with cirrhosis by quantitatively examining the pathophysiological information associated with cirrhosis. The predicted plasma exposure to lornoxicam was approximately 1.12-2.83 times higher in the CYP2C9*1/*3 and *1/*13 groups than in the CYP2C9*1/*1 group of healthy adult populations and patients with cirrhosis. The predicted plasma exposure to lornoxicam was approximately 1.28-3.61 times higher in patients with cirrhosis than in healthy adult populations. If the relationship between lornoxicam exposure in plasma and drug efficacy was proportional, then the proposed adjusted doses of lornoxicam for each group varied from 1.25 mg to 8 mg. As the severity of cirrhosis increased, or when the CYP2C9 genotype was *1 heterozygous, the dose adjustment range of lornoxicam increased. Therefore, the effect of pathophysiological factors (cirrhosis severity) on the pharmacokinetics of lornoxicam might be more important than that of CYP2C9 genetic factors. These results could be useful for broadening the scope of clinical application of lornoxicam by enabling dose selection based on CYP2C9 genotypes and liver cirrhosis degree.

Use of modeling and simulation to predict the influence of triazole antifungal agents on the pharmacokinetics of zanubrutinib and acalabrutinib

Background: Bruton’s tyrosine kinase (BTK) inhibitors are commonly used in the targeted therapy of B-cell malignancies. It is reported that myelosuppression and fungal infections might occur during antitumor therapy of BTK inhibitors, therefore a combination therapy with triazole antifungals is usually required.

Objective: To evaluate the influence of different triazoles (voriconazole, fluconazole, itraconazole) on the pharmacokinetics of BTK inhibitors (zanubrutinib, acalabrutinib) and to quantify the drug-drug interactions (DDIs) between them.

Methods: The physiologically-based pharmacokinetic (PBPK) models were developed based on pharmacokinetic parameters and physicochemical data using Simcyp^® software. These models were validated using clinically observed plasma concentrations data which based on existing published studies. The successfully validated PBPK models were used to evaluate and predict potential DDIs between BTK inhibitors and different triazoles. BTK inhibitors and triazole antifungal agents were simulated by oral administration.

Results: Simulated plasma concentration-time profiles of the zanubrutinib, acalabrutinib, voriconazole, fluconazole, and itraconazole are consistent with the clinically observed profiles which based on existing published studies, respectively. The exposures of BTK inhibitors increase by varying degrees when co-administered with different triazole antifungals. At multiple doses regimen, voriconazole, fluconazole and itraconazole may increase the area under plasma concentration-time curve (AUC) of zanubrutinib by 127%, 81%, and 48%, respectively, and may increase the AUC of acalabrutinib by 326%, 119%, and 264%, respectively.

Conclusion: The PBPK models sufficiently characterized the pharmacokinetics of BTK inhibitors and triazole antifungals, and were used to predict untested clinical scenarios. Voriconazole exhibited the greatest influence on the exposures of BTK inhibitors. The dosage of zanubrutinib or acalabrutinib need to be reduced when co-administered with moderate CYP3A inhibitors.

Physiologically-Based Pharmacokinetic Modeling to Predict Methylphenidate Exposure Affected by Interplay Among Carboxylesterase 1 Pharmacogenetics, Drug-Drug Interactions, and Sex

BACKGROUND AND OBJECTIVE

The pharmacokinetics (PK) of methylphenidate (MPH) differ significantly among individuals. Carboxylesterase 1 (CES1) is the primary enzyme metabolizing MPH, and its function is affected by genetic variants, drug-drug interaction (DDI), and sex. The object of this study is to evaluate CES1 pharmacogenetics as related to MPH metabolism using human liver samples and develop a physiologically-based pharmacokinetic (PBPK) modeling approach to investigate the influence of CES1 genotypes and other factors on MPH PK.

METHODS

The effect of the CES1 variant G143E (rs71647871) on MPH metabolism was studied utilizing 102 individual human liver S9 (HLS9) fraction samples. PBPK models were developed using the population-based PBPK software PK-Sim® by incorporating the HLS9 incubation data. The established models were applied to simulate MPH PK profiles under various clinical scenarios, including different genotypes, drug-alcohol interactions, and the difference between males and females.

RESULTS

The HLS9 incubation study showed that subjects heterozygous for the CES1 variant G143E metabolized MPH at a rate of approximately 50% of that in non-carriers. The developed PBPK models successfully predicted the exposure alteration of MPH from the G143E genetic variant, ethanol-MPH DDI, and sex. Importantly, the study suggests that male G143E carriers who are alcohol consumers are at a higher risk of MPH overexposure.

CONCLUSION

PBPK modeling provides a means for better understanding the mechanisms underlying interindividual variability in MPH PK and PD and could be utilized to develop a safer and more effective MPH pharmacotherapy regimen.

Columbus' egg: Oral drugs are absorbed in finite time

The infinite time of oral drug absorption was conceived from the first day of the birth of pharmacokinetics when H. Dost introduced the term pharmacokinetics in his book published in 1953. He adopted the function developed by H. Bateman back in 1908 for the decay of the nuclei isotopes to describe oral drug absorption as a first-order process. We unveiled this false hypothesis relying on common wisdom i.e. drugs are absorbed in finite time. This false assumption had dramatic effects on the evolution of oral pharmacokinetics but most importantly on the bioavailability and bioequivalence concepts and metrics. This work focuses on the finite absorption time (FAT) concept, the relevant Physiologically Based Finite Time (PBFTPK) models developed and their applications in oral pharmacokinetics, bioavailability and bioequivalence. The crux of the matter is that drug absorption from the gastrointestinal tract takes place under sink conditions because of the high blood flow rate in the vena cava. The termination of oral, pulmonary and intranasal drug absorption at a specific time point, calls for regulatory changes in bioavailability and bioequivalence studies in terms of the study design and metrics used for the bioequivalence assessment.

Determination of the Number of Tissue Groups of Kinetically Distinct Transit Time in Whole-Body Physiologically Based Pharmacokinetic (PBPK) Models II: Practical Application of Tissue Lumping Theories for Pharmacokinetics of Various Compounds

In our companion paper, we described the theoretical basis for tissue lumping in whole-body physiologically based pharmacokinetic (WB-PBPK) models and found that K_det, a coefficient for determining the number of tissue groups of distinct transit time in WB-PBPK models, was related to the fractional change in the terminal slope (FCT) when tissues were progressively lumped from the longest transit time to shorter ones. This study was conducted to identify the practical threshold of K_det by applying the lumping theory to plasma/blood concentration-time relationships of 113 model compounds collected from the literature. We found that drugs having K_det < 0.3 were associated with FCT < 0.1 even when all peripheral tissues were lumped, resulting in comparable plasma concentration-time profiles between one-tissue minimal PBPK (mPBPK) and WB-PBPK models. For drugs with K_det ≥ 1, WB-PBPK profiles appeared similar with two-tissue mPBPK models by applying the rule of FCT < 0.1 for lumping slowly equilibrating tissues. The two-tissue mPBPK model also appeared appropriate in terms of concentration-time profiles for drugs with 0.3 ≤ K_det < 1, although, some compounds (15.9% of the total cases), but not all, in this range showed a slight (maximum of 18.9% of the total AUC) deviation from WB-PBPK models, indicating that the two-tissue model, with caution, could still be used for those cases. Comparison of kinetic parameters between traditional (model-fitting) and current (theoretical calculation) mPBPK analyses revealed their significant correlations. Collectively, these observations suggest that the number of tissue groups could be determined based on the K_det/FCT criteria, and plasma concentration-time profiles from WB-PBPK could be calculated using equations significantly less complex.

A compatibility evaluation between the physiologically based pharmacokinetic (PBPK) model and the compartmental PK model using the lumping method with real cases

Pharmacokinetic (PK) modeling is a useful method for investigating drug absorption, distribution, metabolism, and excretion. The most commonly used mathematical models in PK modeling are the compartment model and physiologically based pharmacokinetic (PBPK) model. Although the theoretical characteristics of each model are well known, there have been few comparative studies of the compatibility of the models. Therefore, we evaluated the compatibility of PBPK and compartment models using the lumping method with 20 model compounds. The PBPK model was theoretically reduced to the lumped model using the principle of grouping tissues and organs that show similar kinetic behaviors. The area under the concentration-time curve (AUC) based on the simulated concentration and PK parameters (drug clearance [CL], central volume of distribution [Vc], peripheral volume of distribution [Vp]) in each model were compared, assuming administration to humans. The AUC and PK parameters in the PBPK model were similar to those in the lumped model within the 2-fold range for 17 of 20 model compounds (85%). In addition, the relationship of the calculated Vd/fu (volume of distribution [Vd], drug-unbound fraction [fu]) and the accuracy of AUC between the lumped model and compartment model confirmed their compatibility. Accordingly, the compatibility between PBPK and compartment models was confirmed by the lumping method. This method can be applied depending on the requirement of compatibility between the two models.

Multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) model to predict local and systemic exposure of drug products applied on skin

Physiologically-based pharmacokinetic models combine knowledge about physiology, drug product properties, such as physicochemical parameters, absorption, distribution, metabolism, excretion characteristics, formulation attributes, and trial design or dosing regimen to mechanistically simulate drug pharmacokinetics (PK). The current work describes the development of a multiphase, multilayer mechanistic dermal absorption (MPML MechDermA) model within the Simcyp Simulator capable of simulating uptake and permeation of drugs through human skin following application of drug products to the skin. The model was designed to account for formulation characteristics as well as body site- and sex- population variability to predict local and systemic bioavailability. The present report outlines the structure and assumptions of the MPML MechDermA model and includes results from simulations comparing absorption at multiple body sites for two compounds, caffeine and benzoic acid, formulated as solutions. Finally, a model of the Feldene (piroxicam) topical gel, 0.5% was developed and assessed for its ability to predict both plasma and local skin concentrations when compared to in vivo PK data.

Application of Physiologically Based Pharmacokinetic Modeling of Lamotrigine Using PK-Sim in Predicting the Impact of Drug Interactions and Dosage Adjustment

Background

Physiologically based pharmacokinetic (PBPK) models are helpful as mechanistic representations of pharmacokinetic parameters. There were no reports of lamotrigine (LTG) PBPK models developed in open source platforms like PK-Sim.

Objectives

The present work was aimed to build a LTG PBPK model and compare it to the clinical data from South Asian Indian patients and use this model to understand the drug interactions of LTG and explore the optimal doses.

Methods and Material

The PBPK model was developed using the PK-Sim software platform and qualified with LTG plasma concentration data from an Indian study. The European population database was chosen as the patient setting in the software. Physicochemical data of LTG and enzyme kinetic data were incorporated from the literature. Dosing protocols were as per the previous study. Interaction models for drug interactions with carbamazepine and valproate were also simulated.

Results

Most of the model predicted concentration-time profiles of LTG at steady-state were well within the observed concentrations. The developed models were suitably qualified. The drug interaction model was used to assess the impact of induction and inhibition of the pharmacokinetic profile of LTG.

Conclusions

The predicted plasma concentrations of the developed PBPK models using the European population database were very similar to the data from Indian patients. The developed LTG PBPK models are applicable in predicting the impact of drug interactions and can yield appropriate LTG doses to be administered.

Therapeutic Drug Monitoring of Vancomycin in Jordanian Patients. Development of Physiologically-Based Pharmacokinetic (PBPK) Model and Validation of Class II Drugs of Salivary Excretion Classification System (SECS)

Vancomycin is a commonly used antibiotic for multi-drug resistant gram-positive infections treatment, especially methicillin-resistant Staphylococcus aureus (MRSA). Despite that, it has wide individual pharmacokinetic variability and nephrotoxic effect. Vancomycin trough concentrations for 57 Jordanian patients were measured in plasma and saliva through immunoassay and liquid chromatography-mass spectrometry (LC-MS/MS), respectively. Plasma levels were within accepted normal range, with exception of 6 patients who showed trough levels of more than 20 μg/ml and vancomycin was discontinued. Bayesian dose-optimizing software was used for patient-specific pharmacokinetics prediction and AUC/MIC calculation. Physiological-based pharmacokinetic (PBPK) vancomycin model was built and validated through GastroPlus™ 9.8 using in-house plasma data. A weak correlation coefficient of 0.2478 (P=0.1049) was found between plasma and saliva concentrations. The suggested normal saliva trough range of vancomycin is 0.01906 to 0.028589 (μg/ml). Analysis of variance showed significant statistical effects of creatinine clearance and albumin concentration on dose-normalized Cmin plasma and saliva levels respectively, which is in agreement with PBPKmodeling. It can be concluded that saliva is not a suitable matrix for TDM of vancomycin. Trough levels in plasma matrix should always be monitored for the safety of patients.