Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends

PBPK modelling for the evaluation of drug-drug interaction between meropenem and valproic acid

AIMS

The present study aimed to quantitatively investigate the potential drug-drug interaction (DDI) mechanism between meropenem (MEPM) and valproic acid (VPA).

METHODS

In the present study, we firstly developed a physiologically based pharmacokinetic (PBPK) model of MEPM and VPA. The verified PBPK model was then used to quantitatively investigate the potential DDI between MEPM and VPA. The effect of genetic polymorphisms of acylpeptide hydrolase (APEH) on DDI was also evaluated.

RESULTS

In our simulation, the plasma concentration of hydrolysate of VPAG decreased to 63% when combined with MEPM. Total plasma concentration of VPA before carbapenem use was 53.61 mg/L, whereas it was 45.42 mg/L during carbapenem use. The inhibition of hydrolysis of VPAG by MEPM alone could not result in a rapid and substantial decrease in the plasma concentration of VPA. Parameter sensitivity analysis showed that the changes of absorption played an important role in the maximum plasma concentration (Cmax) of VPA, whereas area under the plasma concentration-time profile (AUC) was more susceptible to elimination changes. In addition, a decrease in APEH activity had little impact on the plasma pharmacokinetics of VPA.

CONCLUSIONS

The DDI between MEPM and VPA might be a comprehensive result of multiple factors. On the basis of our simulation, interval medication of MEPM injection and VPA immediate release tablet at 4-6 h timed interval was recommended, or intravenous administration of VPA solution was preferred when combination regimen was necessary in a clinical setting.

A preliminary exploration of liver microsomes and PBPK to uncover the impact of CYP3A4/5 and CYP2C19 on tacrolimus and voriconazole drug-drug interactions

Solid transplant recipients are at increased risk for invasive aspergillosis. Tacrolimus and Voriconazole is one of the most frequently utilized treatments for those recipients with invasive fungal infections. However, it is difficult to use them properly due to the interaction between them. This study aimed to investigate the potential drug-drug interaction between Tacrolimus and Voriconazole by multiple methods, including in vitro liver microsome method and the PBPK(Physiologically Based Pharmacokinetic) model. Midazolam and testosterone were used as probe substrates to evaluate individual differences in CYP3A4/5 metabolic activity. A comprehensive interaction analysis was also conducted based on the STITCH database and the DD-Inter system. Furthermore, a PBPK model was constructed by the data from the literature to simulate the real metabolic process in vivo. The research employed multiple methodologies to demonstrate that the co-administration of Voriconazole significantly enhances Tacrolimus concentrations, considering genotypes and the activity of CYP3A4/5 genotypes. The findings indicated a decrease in the relative percentages of midazolam and testosterone metabolites with increasing Voriconazole concentration. Moreover, the results for residual Tacrolimus in the 30-minute incubation group revealed that Voriconazole exerts a mild inhibitory effect on the in vitro metabolism of Tacrolimus. The STITCH database and DD-Inter system analysis also suggested that Tacrolimus and Voriconazole share a strong association in liver metabolism, most likely interacting with CYP3A4/5 and CYP2C19. Furthermore, the result of PBPK analysis indicated that Tacrolimus AUC increases with Voriconazole co-therapy. Moreover, the AUC of Tacrolimus in intermediate CYP2C19 metabolizers (IM) was the highest at 10.1 µmol·min/L, followed by poor metabolizers (PM) at 8.13 µmol·min/L, and extensive metabolizers (EM) at 2.18 µmol·min/L. And the genotype of CYP3A5 poor metabolizer (PM) had AUC of Tacrolimus at 3.13µmol·min/L and extensive metabolizer (EM) at 2.18µmol·min/L. Microsomal studies, PBPK models, and multiple other analyses have comprehensively elucidated the impact of Voriconazole on Tacrolimus concentrations. These findings can serve as a valuable point of reference for concurrently administering these two medications. These findings also indicate that the genotypes of CYP2C19 play an important role in the development of DDI during concurrent Tacrolimus and Voriconazole treatment, which may have some guidance for clinical medication.

Use of an extensively humanized mouse model to predict the risk of drug-drug interactions in patients receiving dexamethasone

The corticosteroid dexamethasone, which is used to treat numerous health conditions, remains the first-line treatment for patients hospitalized with COVID-19 requiring oxygen. Current British National Formulary prescribing advice warns of a "severe theoretical" or "severe anecdotal" risk of drug-drug interactions between dexamethasone and 138 different medications. In humans, dexamethasone is eliminated via the cytochrome P450 monooxygenase system, particularly CYP3A4. It is also described as a human cytochrome P450-inducing agent. To establish factors that affect concomitant therapy and dexamethasone efficacy in the treatment of COVID-19, we used a unique mouse model humanized for cytochrome P450s and the transcription factors that regulate their expression, the pregnane X receptor, and the constitutive androstane receptor. We found that induction of CYP3A4 with the anticancer drug dabrafenib or the herbal medicine St John's wort profoundly reduced dexamethasone exposure. These data suggest that comedications that induce cytochrome P450 expression can have a marked effect on dexamethasone exposure and, potentially, clinical efficacy. We also observed that rather than increasing CYP3A4 expression, dexamethasone at doses equivalent to or higher than those used in the treatment of COVID-19 reduced CYP3A4 expression and increased exposure to dabrafenib. These data indicate the need for a clinical trial to establish the risk of overexposure to comedications during dexamethasone treatment, including the treatment of COVID-19. SIGNIFICANCE STATEMENT: Current prescribing advice identifies a potential theoretical risk of severe side effects when dexamethasone, one of the most widely used drugs in clinical practice, is coadministered with many other drugs; it is, however, difficult to define the magnitude of this risk for specific drug combinations. We describe the use of cytochrome P450-humanized 8HUM mice to predict drug-drug interactions in patients on polypharmacy, a means of generating data that could better inform clinicians regarding foreseeable drug-drug interactions involving dexamethasone.

Development of a physiologically based pharmacokinetic model for flunixin in cattle and swine following dermal exposure

Flunixin meglumine is a nonsteroidal anti-inflammatory drug (NSAID). Banamine Transdermal is a pour-on formulation of flunixin approved for pain control in beef and dairy cattle, but not for calves and some classes of dairy cattle or swine. Violative flunixin residues in edible tissues in cattle and swine have been reported and are usually attributed to non-compliant drug use or failure to observe an appropriate withdrawal time. This project aimed to develop a physiologically based pharmacokinetic (PBPK) model for flunixin in cattle and swine to predict withdrawal intervals (WDI) after exposures to different therapeutic regimens of Banamine Transdermal. Due to the lack of comprehensive skin physiological data in cattle, the model was initially developed for swine and then adapted for cattle. Monte Carlo simulation was employed for population variability analysis. The model predicted WDIs were rounded to 1 and 2 d for liver and muscle in cattle, respectively, under FDA tolerance levels, while under EU maximum residue limits, the WDIs were rounded to 1, 3, 2, and 2 d for liver, kidney, muscle, and fat, respectively, following a labeled single transdermal 3.3 mg/kg dose in cattle. The model was converted into a user-friendly interactive PBPK (iPBPK) interface. This study reports the first transdermal absorption model for drugs in cattle. This iPBPK model provides a scientifically based tool for the prediction of WDIs in cattle and swine administered with flunixin in an extra-label manner, especially by the transdermal route.

Utility of physiologically based pharmacokinetic modeling in predicting and characterizing clinical drug interactions

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions (DDIs). With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late-stage drug development and is often used to support regulatory packages for new drug applications. This Minireview will briefly describe the approaches to predicting DDI using PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models, and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations, and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. SIGNIFICANCE STATEMENT: Physiologically based pharmacokinetic (PBPK) models have been a key tool in the characterization of various pharmacokinetic phenomena, including drug-drug interactions. This Minireview will highlight recent advancements and publications around physiologically based pharmacokinetic drug-drug interaction modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

The absorption, distribution, metabolism and elimination characteristics of small interfering RNA therapeutics and the opportunity to predict disposition in pregnant women

Small interfering RNA (siRNA) therapeutics represent an emerging class of pharmacotherapy with the potential to address previously hard-to-treat diseases. Currently approved siRNA therapeutics include lipid nanoparticle-encapsulated siRNA and tri-N-acetylated galactosamine-conjugated siRNA. These siRNA therapeutics exhibit distinct pharmacokinetic characteristics and unique absorption, distribution, metabolism, and elimination (ADME) properties. As a new drug modality, limited clinical data are available for siRNA therapeutics in specific populations, including pediatrics, geriatrics, individuals with renal or hepatic impairment, and pregnant women, making dosing challenging. In this Minireview, a mechanistic overview of the ADME properties of the 5 currently approved siRNA therapeutics is presented. A concise overview of the clinical data available for therapeutic siRNAs in special populations, focusing on the potential impact of physiologic changes during pregnancy on siRNA disposition, is provided. The utility of physiologically based pharmacokinetic (PBPK) modeling as a tool to elucidate the characteristics and disposition of siRNA therapeutics in pregnant women is explored. Additionally, opportunities to integrate known physiologic alterations induced by pregnancy into PBPK models that incorporate siRNA ADME mechanisms to predict the effects of pregnancy on siRNA disposition are discussed. Clinical data regarding the use of therapeutic siRNA in special populations remain limited. Data for precise parameterization of maternal-fetal siRNA PBPK models are lacking presently and underscore the need for further research in this area. Addressing this gap in knowledge will not only enhance our understanding of siRNA pharmacokinetics during pregnancy but also advance the possible development of siRNA therapeutics to treat pregnancy-related conditions. SIGNIFICANCE STATEMENT: This Minireview proposes a framework on how small interfering RNA (siRNA) disposition can be predicted in pregnancy based on mechanistic absorption, distribution, metabolism, and elimination (ADME) information using physiologically-based pharmacokinetic (PBPK) modeling. The mechanistic ADME information and available clinical data in special populations of currently Food and Drug Administration-approved siRNA therapeutics are summarized. Additionally, how physiological changes during pregnancy may affect siRNA disposition is reviewed, and the opportunities to use PBPK modeling to predict siRNA disposition in pregnant women is explored.

Potential and challenges in application of physiologically based pharmacokinetic modeling in predicting diarrheal disease impact on oral drug pharmacokinetics

Physiologically based pharmacokinetic (PBPK) modeling is a physiologically relevant approach that integrates drug-specific and system parameters to generate pharmacokinetic predictions for target populations. It has gained immense popularity for drug-drug interaction, organ impairment, and special population studies over the past 2 decades. However, an application of PBPK modeling with great potential remains rather overlooked-prediction of diarrheal disease impact on oral drug pharmacokinetics. Oral drug absorption is a complex process involving the interplay between physicochemical characteristics of the drug and physiological conditions in the gastrointestinal tract. Diarrhea, a condition common to numerous diseases impacting many worldwide, is associated with physiological changes in many processes critical to oral drug absorption. In this Minireview, we outline key processes governing oral drug absorption, provide a high-level overview of key parameters for modeling oral drug absorption in PBPK models, examine how diarrheal diseases may impact these processes based on literature findings, illustrate the clinical relevance of diarrheal disease impact on oral drug absorption, and discuss the potential and challenges of applying PBPK modeling in predicting disease impacts. SIGNIFICANCE STATEMENT: Pathophysiological changes resulting from diarrheal diseases can alter important factors governing oral drug absorption, contributing to suboptimal drug exposure and treatment failure. Physiologically based pharmacokinetic (PBPK) modeling is an in silico approach that has been increasingly adopted for drug-drug interaction potential, organ impairment, and special population assessment. This Minireview highlights the potential and challenges of using physiologically based pharmacokinetic modeling as a tool to improve our understanding of how diarrheal diseases impact oral drug pharmacokinetics.

Advancing understanding of human variability through toxicokinetic modeling, in vitro-in vivo extrapolation, and new approach methodologies

The merging of physiology and toxicokinetics, or pharmacokinetics, with computational modeling to characterize dosimetry has led to major advances for both the chemical and pharmaceutical research arenas. Driven by the mutual need to estimate internal exposures where in vivo data generation was simply not possible, the application of toxicokinetic modeling has grown exponentially in the past 30 years. In toxicology the need has been the derivation of quantitative estimates of toxicokinetic and toxicodynamic variability to evaluate the suitability of the tenfold uncertainty factor employed in risk assessment decision-making. Consideration of a host of physiologic, ontogenetic, genetic, and exposure factors are all required for comprehensive characterization. Fortunately, the underlying framework of physiologically based toxicokinetic models can accommodate these inputs, in addition to being amenable to capturing time-varying dynamics. Meanwhile, international interest in advancing new approach methodologies has fueled the generation of in vitro toxicity and toxicokinetic data that can be applied in in vitro-in vivo extrapolation approaches to provide human-specific risk-based information for historically data-poor chemicals. This review will provide a brief introduction to the structure and evolution of toxicokinetic and physiologically based toxicokinetic models as they advanced to incorporate variability and a wide range of complex exposure scenarios. This will be followed by a state of the science update describing current and emerging experimental and modeling strategies for population and life-stage variability, including the increasing application of in vitro-in vivo extrapolation with physiologically based toxicokinetic models in pharmaceutical and chemical safety research. The review will conclude with case study examples demonstrating novel applications of physiologically based toxicokinetic modeling and an update on its applications for regulatory decision-making. Physiologically based toxicokinetic modeling provides a sound framework for variability evaluation in chemical risk assessment.

Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure-activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer.

Insights into pharmacogenetics, drug-gene interactions, and drug-drug-gene interactions

This review explores genetic contributors to drug interactions, known as drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively). This article is part of a mini-review issue led by the International Society for the Study of Xenobiotics (ISSX) New Investigators Group. Pharmacogenetics (PGx) is the study of the impact of genetic variation on pharmacokinetics (PK), pharmacodynamics (PD), and adverse drug reactions. Genetic variation in pharmacogenes, including drug metabolizing enzymes and drug transporters, is common and can increase the risk of adverse drug events or contribute to reduced efficacy. In this review, we summarize clinically actionable genetic variants, and touch on methodologies such as genotyping patient DNA to identify genetic variation in targeted genes, and deep mutational scanning as a high-throughput in vitro approach to study the impact of genetic variation on protein function and/or expression in vitro. We highlight the utility of physiologically based pharmacokinetic (PBPK) models to integrate genetic and chemical inhibitor and inducer data for more accurate human PK simulations. Additionally, we analyze the limitations of historical ethnic descriptors in pharmacogenomics research. Altogether, the work herein underscores the importance of identifying and understanding complex DGI and DDGIs with the intention to provide better treatment outcomes for patients. We also highlight current barriers to wide-scale implementation of PGx-guided dosing as standard or care in clinical settings.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

A genetic algorithm-based approach for the prediction of metabolic drug-drug interactions involving CYP2C8 or CYP2B6

BACKGROUND AND OBJECTIVES

A genetic algorithm (GA) approach was developed to predict drug-drug interactions (DDIs) caused by cytochrome P450 2C8 (CYP2C8) inhibition or cytochrome P450 2B6 (CYP2B6) inhibition or induction. Nighty-eight DDIs, obtained from published in vivo studies in healthy volunteers, have been considered using the area under the plasma drug concentration-time curve (AUC) ratios (i.e., ratios of AUC of the drug substrate administered in combination with a DDI perpetrator to AUC of the drug substrate administered alone) to describe the extent of DDI.

METHODS

The following parameters were estimated in this approach: the contribution ratios (CRCYP2B6 and CRCYP2C8, i.e., the fraction of the dose metabolized via CYP2B6 or CYP2C8, respectively) and the inhibitory or inducing potency of the perpetrator drug (IRCYP2B6, IRCYP2C8 and ICCYP2B6, for inhibition of CYP2B6 and CYP2C8, and induction of CYP2B6, respectively). The workflow consisted of three main phases. First, the initial estimates of the parameters were estimated through GA. Then, the model was validated using an external validation. Finally, the parameter values were refined via a Bayesian orthogonal regression using all data.

RESULTS

The AUC ratios of 5 substrates, 11 inhibitors and 19 inducers of CYP2B6, and the AUC ratios of 19 substrates and 23 inhibitors of CYP2C8 were successfully predicted by the developed methodology within 50-200% of observed values.

CONCLUSIONS

The approach proposed in this work may represent a useful tool for evaluating the suitable doses of a CYP2C8 or CYP2B6 substrates co-administered with perpetrators.

Nano- and microplastic PBK modeling in the context of human exposure and risk assessment

Insufficient data on nano- and microplastics (NMP) hinder robust evaluation of their potential health risks. Methodological disparities and the absence of established toxicity thresholds impede the comparability and practical application of research findings. The diverse attributes of NMP, such as variations in sizes, shapes, and compositions, complicate human health risk assessment. Although probability density functions (PDFs) show promise in capturing this diversity, their integration into risk assessment frameworks is limited. Physiologically based kinetic (PBK) models offer a potential solution to bridge the gap between external exposure and internal dosimetry for risk evaluation. However, the heterogeneity of NMP poses challenges for accurate biodistribution modeling. A literature review, encompassing both experimental and modeling studies, was conducted to examine biodistribution studies of monodisperse micro- and nanoparticles. The literature search in PubMed and Scopus databases yielded 39 studies that met the inclusion criteria. Evaluation criteria were adapted from previous Quality Assurance and Quality Control (QA-QC) studies, best practice guidelines from WHO (2010), OECD guidance (2021), and additional criteria specific to NMP risk assessment. Subsequently, a conceptual framework for a comprehensive NMP-PBK model was developed, addressing the multidimensionality of NMP particles. Parameters for an NMP-PBK model are presented. QA-QC evaluations revealed that most experimental studies scored relatively well (>0) in particle characterizations and environmental settings but fell short in criteria application for biodistribution modeling. The evaluation of modeling studies revealed that information regarding the model type and allometric scaling requires improvement. Three potential applications of PDFs in PBK modeling of NMP are identified: capturing the multidimensionality of the NMP continuum, quantifying the probabilistic definition of external exposure, and calculating the bio-accessibility fraction of NMP in the human body. A framework for an NMP-PBK model is proposed, integrating PDFs to enhance the assessment of NMP's impact on human health.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Prediction of drug-drug interactions between roflumilast and CYP3A4/1A2 perpetrators using a physiologically-based pharmacokinetic (PBPK) approach

This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model to predict changes in the pharmacokinetics (PK) and pharmacodynamics (PD, PDE4 inhibition) of roflumilast (ROF) and ROF N-oxide when co-administered with eight CYP3A4/1A2 perpetrators. The population PBPK model of ROF and ROF N-oxide has been successfully developed and validated based on the four clinical PK studies and five clinical drug-drug interactions (DDIs) studies. In PK simulations, every ratio of prediction to observation for PK parameters fell within the range 0.7 to 1.5. In DDI simulations, except for tow peak concentration ratios (Cmax) of ROF with rifampicin (prediction: 0.63 vs. observation: 0.19) and with cimetidine (prediction: 1.07 vs. observation: 1.85), the remaining predicted ratios closely matched the observed ratios. Additionally, the PBPK model suggested that co-administration with the three perpetrators (cimetidine, enoxacin, and fluconazole) may use with caution, with CYP3A4 strong inhibitor (ketoconazole and itraconazole) or with dual CYP3A41A2 inhibitor (fluvoxamine) may reduce to half-dosage or use with caution, while co-administration with CYP3A4 strong or moderate inducer (rifampicin, efavirenz) should avoid. Overall, the present PBPK model can provide recommendations for adjusting dosing regimens in the presence of DDIs.

Research Methods and New Advances in Drug-Drug Interactions Mediated by Renal Transporters

The kidney is critical in the human body's excretion of drugs and their metabolites. Renal transporters participate in actively secreting substances from the proximal tubular cells and reabsorbing them in the distal renal tubules. They can affect the clearance rates (CLr) of drugs and their metabolites, eventually influence the clinical efficiency and side effects of drugs, and may produce drug-drug interactions (DDIs) of clinical significance. Renal transporters and renal transporter-mediated DDIs have also been studied by many researchers. In this article, the main types of in vitro research models used for the study of renal transporter-mediated DDIs are membrane-based assays, cell-based assays, and the renal slice uptake model. In vivo research models include animal experiments, gene knockout animal models, positron emission tomography (PET) technology, and studies on human beings. In addition, in vitro-in vivo extrapolation (IVIVE), ex vivo kidney perfusion (EVKP) models, and, more recently, biomarker methods and in silico models are included. This article reviews the traditional research methods of renal transporter-mediated DDIs, updates the recent progress in the development of the methods, and then classifies and summarizes the advantages and disadvantages of each method. Through the sorting work conducted in this paper, it will be convenient for researchers at different learning stages to choose the best method for their own research based on their own subject's situation when they are going to study DDIs mediated by renal transporters.

Prediction of Omeprazole Pharmacokinetics and its Inhibition on Gastric Acid Secretion in Humans Using Physiologically Based Pharmacokinetic-Pharmacodynamic Model Characterizing CYP2C19 Polymorphisms

PURPOSE

To develop a whole physiologically based pharmacokinetic-pharmacodynamic (PBPK-PD) model to describe the pharmacokinetics and anti-gastric acid secretion of omeprazole in CYP2C19 extensive metabolizers (EMs), intermediate metabolizers (IMs), poor metabolizers (PMs) and ultrarapid metabolizers (UMs) following oral or intravenous administration.

METHODS

A PBPK/PD model was built using Phoenix WinNolin software. Omeprazole was mainly metabolized by CYP2C19 and CYP3A4 and the CYP2C19 polymorphism was incorporated using in vitro data. We described the PD by using a turn-over model with parameter estimates from dogs and the effect of a meal on the acid secretion was also implemented. The model predictions were compared to 53 sets of clinical data.

RESULTS

Predictions of omeprazole plasma concentration (72.2%) and 24 h stomach pH after administration (85%) were within 0.5-2.0-fold of the observed values, indicating that the PBPK-PD model was successfully developed. Sensitivity analysis demonstrated that the contributions of the tested factors to the plasma concentration of omeprazole were Vmax,2C19 ≈ Papp > Vmax,3A4 > Kti, and contributions to its pharmacodynamic were Vmax,2C19 > kome > kms > Papp > Vmax,3A4. The simulations showed that while the initial omeprazole dose in UMs, EMs, and IMs increased 7.5-, 3- and 1.25-fold compared to those of PMs, the therapeutic effect was similar.

CONCLUSIONS

The successful establishment of this PBPK-PD model highlights that pharmacokinetic and pharmacodynamic profiles of drugs can be predicted using preclinical data. The PBPK-PD model also provided a feasible alternative to empirical guidance for the recommended doses of omeprazole.

Physiologically based pharmacokinetic modelling to predict drug-drug interactions for encorafenib. Part I. Model building, validation, and prospective predictions with enzyme inhibitors, inducers, and transporter inhibitors

Encorafenib, a potent BRAF kinase inhibitor undergoes significant metabolism by CYP3A4 (83%) and CYP2C19 (16%) and also a substrate of P-glycoprotein (P-gp). Because of this, encorafenib possesses potential for enzyme-transporter related interactions. Clinically, its drug-drug interactions (DDIs) with CYP3A4 inhibitors (posaconazole, diltiazem) were reported and hence there is a necessity to study DDIs with multiple enzyme inhibitors, inducers, and P-gp inhibitors.USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors, CYP3A4 inducers were selected and prospective DDIs were simulated using physiologically based pharmacokinetic modelling (PBPK). Impact of dose (50 mg vs. 300 mg) and staggering of administrations (0-10 h) on the DDIs were predicted.PBPK models for encorafenib, perpetrators simulated PK parameters within twofold prediction error. Clinically reported DDIs with posaconazole and diltiazem were successfully predicted.CYP2C19 inhibitors did not result in significant DDI whereas strong CYP3A4 inhibitors resulted in DDI ratio up to 4.5. Combining CYP3A4, CYP2C19 inhibitors yielded DDI equivalent CYP3A4 alone. Strong CYP3A4 inducers yielded DDI ratio up to 0.3 and no impact of P-gp inhibitors on DDIs was observed. The DDIs were not impacted by dose and staggering of administration. Overall, this work indicated significance of PBPK modelling for evaluating clinical DDIs with enzymes, transporters and interplay.

Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review

The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure-activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.

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.

Evaluating the impact of co-administered drug and disease on ripretinib exposure: A physiologically-based pharmacokinetic modeling approach

Ripretinib, as an oral kinase inhibitor, has been approved to treat advanced gastrointestinal stromal tumors (GIST) and is often used in combination with other drugs to slow disease progression, thus potential drug-drug Interactions (DDIs) and drug-disease interactions (DDZIs) have received much attention. To guide clinical rational drug use, this study assessed the effect of co-administered drugs and diseases on ripretinib exposure. Simcyp® Simulator was used to develop the physiologically-based pharmacokinetic (PBPK) model of ripretinib, which was validated and refined with clinical data. We then examined the impact of several CYP3A4 inhibitors and inducers as well as different diseases on ripretinib exposure using the validated model. In the DDI simulation, moderate CYP3A4 inhibitors and inducers changed the exposure of ripretinib by 1.25-2 fold. In hepatic impairment (HI), the simulation showed that ripretinib's AUC increased by 32%, 100%, and 152% for Child-Pugh A, B, and C classification while C_max increased by 2%, 10%, and 15%, respectively. In renal impairment (RI), the model-simulated AUC in moderate and severe RIs increased by 27% and 20%. In conclusion, PBPK models demonstrated quantitative prediction of ripretinib's pharmacokinetic changes under varying conditions that might be useful for its rational use.

Experimental and computational models to investigate intestinal drug permeability and metabolism

Oral administration is the preferred route for drug administration that leads to better therapy compliance. The intestine plays a key role in the absorption and metabolism of oral drugs, therefore, new intestinal models are being continuously proposed, which contribute to the study of intestinal physiology, drug screening, drug side effects, and drug-drug interactions.Advances in pharmaceutical processes have produced more drug formulations, causing challenges for intestinal models. To adapt to the rapid evolution of pharmaceuticals, more intestinal models have been created. However, because of the complexity of the intestine, few models can take all aspects of the intestine into account, and some functions must be sacrificed to investigate other areas. Therefore, investigators need to choose appropriate models according to the experimental stage and other requirements to obtain the desired results.To help researchers achieve this goal, this review summarised the advantages and disadvantages of current commonly used intestinal models and discusses possible future directions, providing a better understanding of intestinal models.

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.

In Silico Studies to Support Vaccine Development

The progress that has been made in computer science positioned in silico studies as an important and well-recognized methodology in the drug discovery and development process. It has numerous advantages in terms of costs and also plays a huge impact on the way the research is conducted since it can limit the use of animal models leading to more sustainable research. Currently, human trials are already being partly replaced by in silico trials. EMA and FDA are both endorsing these studies and have been providing webinars and guidance to support them. For instance, PBPK modeling studies are being used to gather data on drug interactions with other drugs and are also being used to support clinical and regulatory requirements for the pediatric population, pregnant women, and personalized medicine. This trend evokes the need to understand the role of in silico studies in vaccines, considering the importance that these products achieved during the pandemic and their promising hope in oncology. Vaccines are safer than other current oncology treatments. There is a huge variety of strategies for developing a cancer vaccine, and some of the points that should be considered when designing the vaccine technology are the following: delivery platforms (peptides, lipid-based carriers, polymers, dendritic cells, viral vectors, etc.), adjuvants (to boost and promote inflammation at the delivery site, facilitating immune cell recruitment and activation), choice of the targeted antigen, the timing of vaccination, the manipulation of the tumor environment, and the combination with other treatments that might cause additive or even synergistic anti-tumor effects. These and many other points should be put together to outline the best vaccine design. The aim of this article is to perform a review and comprehensive analysis of the role of in silico studies to support the development of and design of vaccines in the field of oncology and infectious diseases. The authors intend to perform a literature review of all the studies that have been conducted so far in preparing in silico models and methods to support the development of vaccines. From this point, it was possible to conclude that there are few in silico studies on vaccines. Despite this, an overview of how the existing work could support the design of vaccines is described.

Single-time-point estimation of absorbed doses in PRRT using a non-linear mixed-effects model

INTRODUCTION

Estimation of accurate time-integrated activity coefficients (TIACs) and radiation absorbed doses (ADs) is desirable for treatment planning in peptide-receptor radionuclide therapy (PRRT). This study aimed to investigate the accuracy of a simplified dosimetry using a physiologically-based pharmacokinetic (PBPK) model, a nonlinear mixed effect (NLME) model, and single-time-point imaging to calculate the TIACs and ADs of ⁹⁰Y-DOTATATE in various organs of dosimetric interest and tumors.

MATERIALS & METHODS

Biokinetic data of ¹¹¹In-DOTATATE in tumors, kidneys, liver, spleen, and whole body were obtained from eight patients using planar scintigraphic imaging at T1 = (2.9 ± 0.6), T2 = (4.6 ± 0.4), T3 = (22.8 ± 1.6), T4 = (46.7 ± 1.7) and T5 = (70.9 ± 1.0) h post injection. Serum activity concentration was measured at 5 and 15 min; 0.5, 1, 2, and 4 h; and 1, 2, and 3 d p.i.. A published PBPK model for PRRT, NLME, and a single-time-point imaging datum at different time points were used to calculate TIACs in tumors, kidneys, liver, spleen, whole body, and serum. Relative deviations (RDs) (median [min, max]) between the calculated TIACs from single-time-point imaging were compared to the TIACs calculated from the all-time-points fit. The root mean square error (RMSE) of the difference between the computed ADs from the single-time-point imaging and reference ADs from the all-time point fittings were analyzed. A joint root mean square error RMSE_joint of the ADs was calculated with the RSME from both the tumor and kidneys to sort the time points concerning accurate results for the kidneys and tumor dosimetry. The calculations of TIACs and ADs from the single-time-point dosimetry were repeated using the sum of exponentials (SOE) approach introduced in the literature. The RDs and the RSME of the PBPK approach in our study were compared to the SOE approach.

RESULTS

Using the PBPK and NLME models and the biokinetic measurements resulted in a good fit based on visual inspection of the fitted curves and the coefficient of variation CV of the fitted parameters (<50%). T4 was identified being the time point with a relatively low median and range of TIACs RDs, i.e., 5 [1, 21]% and 2 [-15, 21]% for kidneys and tumors, respectively. T4 was found to be the time point with the lowest joint root mean square error RMSE_joint of the ADs. Based on the RD and RMSE, our results show a similar performance as the SOE and NLME model approach.

SUMMARY

In this study, we introduced a simplified calculation of TIACs/ADs using a PBPK model, an NLME model, and a single-time-point measurement. Our results suggest a single measurement might be used to calculate TIACs/ADs in the kidneys and tumors during PRRT.

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling

Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.

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.

One small step in time, one giant leap for DMPK kind - A CRO perspective of the evolving core discipline of drug development

As the Space Race or Formula 1 drives innovation, efficiency and progress in home technology and home car markets, so Drug Metabolism and Pharmacokinetics (DMPK) drives scientific innovation and value for drug development companies. Stand still and fall behind as the saying goes, and these analogies are true as much in the design and conduct of DMPK studies as they are in technology and manufacturing sectors.This short review showcases the impact that DMPK has had on drug development and how it has changed in the last 10 years, illustrating the value added scientific benefit, cost and time saving, that innovative DMPK program design and study conduct have. Examples and case studies spanning novel in vitro alternatives such as organ-on-a-chip (OOAC) developments; use of in vivo microsampling across small and large animal species; to how challenging historical paradigms in Absorption, Distribution, Metabolism and Excretion (ADME) studies; and embracing new technologies to address regulatory concerns, are presented.The continual pace of change has kept DMPK at the core of pharmaceutical, crop and chemical evaluation, and this is set to continue as regulators use this discipline to inform decision making. With new modalities and new scientific questions, DMPK will continue to evolve, with the likes of new in vitro, in vivo and in silico models becoming central to candidate selection and progression.

Physiologically based pharmacokinetic (PBPK) modeling of flurbiprofen in different CYP2C9 genotypes

The aim of this study was to establish the physiologically based pharmacokinetic (PBPK) model of flurbiprofen related to CYP2C9 genetic polymorphism and describe the pharmacokinetics of flurbiprofen in different CYP2C9 genotypes. PK-Sim® software was used for the model development and validation. A total of 16 clinical pharmacokinetic data for flurbiprofen in different CYP2C9 genotypes, dose regimens, and age groups were used for the PBPK modeling. Turnover number (k_cat) of CYP2C9 values were optimized to capture the observed profiles in different CYP2C9 genotypes. In the simulation, predicted fraction metabolized by CYP2C9, fraction excreted to urine, bioavailability, and volume of distribution were similar to previously reported values. Predicted plasma concentration-time profiles in different CYP2C9 genotypes were visually similar to the observed profiles. Predicted AUC_inf in CYP2C9*1/*2, CYP2C9*1/*3, and CYP2C9*3/*3 genotypes were 1.44-, 2.05-, and 3.67-fold higher than the CYP2C9*1/*1 genotype. The ranges of fold errors for AUC_inf, C_max, and t_1/2 were 0.84-1.00, 0.61-1.22, and 0.74-0.94 in development and 0.59-0.98, 0.52-0.97, and 0.61-1.52 in validation, respectively, which were within the acceptance criterion. Thus, the PBPK model was successfully established and described the pharmacokinetics of flurbiprofen in different CYP2C9 genotypes, dose regimens, and age groups. The present model could guide the decision-making of tailored drug administration strategy by predicting the pharmacokinetics of flurbiprofen in various clinical scenarios.

Impact of route-dependent phase-II gut metabolism and enterohepatic circulation on the bioavailability and systemic disposition of resveratrol in rats and humans: A comprehensive whole body physiologically-based pharmacokinetic modeling

Resveratrol, a natural polyphenolic phytoalexin, is a dietary supplement that improves the outcomes of metabolic, cardiovascular, and other age-related diseases due to its diverse pharmacological activities. Although there have been several preclinical and clinical investigations of resveratrol, the contributions of gut phase-II metabolism and enterohepatic circulation to the oral bioavailability and pharmacokinetics of resveratrol remain unclear. Furthermore, a physiologically-based pharmacokinetic (PBPK) model that accurately describes and predicts the systemic exposure profiles of resveratrol in clinical settings has not been developed. Experimental data were acquired from several perspectives, including in vitro protein binding and blood distribution, in vitro tissue S9 metabolism, in situ intestinal perfusion, and in vivo pharmacokinetics and excretion studies. Using these datasets, an in-house whole-body PBPK model incorporating route-dependent phase-II (glucuronidation and sulfation) gut metabolism and enterohepatic circulation processes was constructed and optimized for chemical-specific parameters. The developed PBPK model aligned with the observed systemic exposure profiles of resveratrol in single and multiple dosing regimens with an acceptable accuracy of 0.538-0.999-fold errors. Furthermore, the model simulations elucidated the substantial contribution of gut first-pass metabolism to the oral bioavailability of resveratrol and suggested differential effects of enterohepatic circulation on the systemic exposure of resveratrol between rats and humans. After partial modification and verification, our proposed PBPK model would be valuable to optimize dosage regimens and predict food-drug interactions with resveratrol-based natural products in various clinical scenarios.