Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine

Modeling biomarker kinetics of Aβ levels in serum following blast

Elucidating the unique neuropathological response to blast exposure remains a barrier towards the development of diagnostic approaches for those with blast-induced traumatic brain injury (bTBI). Quantification of biomarker concentrations in the blood post-injury is typically used to inform brain injury severity. However, injury progression and associated changes in biomarker concentrations are sensitive to parameters such as the blast overpressure (BOP) magnitude and frequency of blast exposure. Through this work, a blast-dose biomarker kinetics (BxK) platform was developed and validated for Aβ42 as a promising predictor of injury post-blast. Blast-dose responses accounting for BOP magnitude and frequency were integrated into a mathematical model accounting for whole-body Aβ peptide kinetics. Validation of the developed model was performed through comparison with acute monomer levels in the blood serum of 15 service members exposed to repeated low-level blast while undergoing three-day weapons training. Amyloid precursor protein (APP) synthesis was assumed to be proportional to blast magnitude and additive effects within a window of recovery were applied to account for cumulative exposure. Aβ42 concentrations in the blood serum were predicted within 6.5 ± 5.2% on average, demonstrating model feasibility and biomarker sensitivity to blast. Outcomes discuss how modulation of patient-specific factors (age, weight, genetic factors, years of exposure, sleep) and pathophysiological factors (BBB permeability, amyloidogenic pathology, neuroinflammation) can reveal potential sources of variability in experimental data and be incorporated into the blast-dose BxK platform in future iterations. Advancements in model complexity accounting for sex-specific factors, weapon system, stress levels, risk of symptom onset, and pharmacological treatment strategies are anticipated to improve model calibration. Utilization of this blast-dose BxK model to identify drivers of pathophysiological mechanisms and predict chronic outcomes has the potential to transform bTBI diagnostic, prognostic, and therapeutic strategies.

Ertapenem in the Context of Hypoalbuminemia: Implications for Critically Ill Patients

The global rise in extended-spectrum β-lactamase-producing Enterobacterales (ESBL-E) has created significant challenges in the management of severe infections, including bacteremia. Ertapenem, a once-daily carbapenem with high protein-binding affinity (85%-95%), is an ideal option for ESBL-E because of its spectrum and dosing convenience. However, hypoalbuminemia, a common condition in critically ill patients that is independently associated with poor outcomes, raises concerns about altered pharmacokinetics, specifically increased free drug fractions, enhanced clearance, and shortened half-life. These pharmacokinetic changes have been hypothesized to lead to suboptimal drug levels and treatment failure, although clinical evidence remains inconsistent. This narrative review examines ertapenem's pharmacokinetic and pharmacodynamic changes in patients with hypoalbuminemia, focusing on its clinical implications. While some studies have reported suboptimal outcomes in critically ill patients, others have demonstrated comparable efficacy to broader spectrum carbapenems when minimum inhibitory concentration values are favorable, and source control is achieved. These findings challenge the concerns raised in the 2024 Infectious Diseases Society of America Gram-negative resistance guidance, which cautions against ertapenem use in patients with hypoalbuminemia. Rather than universally avoiding ertapenem, clinicians should prioritize individualized decision making based on patient-specific factors. Further research is warranted to optimize dosing strategies. However, current data suggest that ertapenem remains a viable and effective option in this high-risk population when used judiciously.

Physiologically Based Pharmacokinetic Model of Cefotaxime in Patients with Impaired Renal Function

BACKGROUND

Cefotaxime is a widely prescribed cephalosporin antibiotic used to treat various infections. It is mainly eliminated unchanged by the kidney through tubular secretion and glomerular filtration. Therefore, a reduction of kidney function may increase exposure to the drug and induce toxic side effects.

OBJECTIVES

The objectives of this study were to develop a physiologically based pharmacokinetic (PBPK) model of cefotaxime in healthy European adults, to mechanistically describe the impact of chronic kidney disease (CKD) on cefotaxime pharmacokinetics, and to assess the applicability of the model to patients requiring intensive care.

METHODS

Using PK-Sim® software, we developed a PBPK model for cefotaxime, including basolateral and apical renal transporters and renal esterases, in healthy subjects and then extrapolated to patients with CKD by incorporating pathophysiological changes and reductions in activity of drug-metabolizing enzymes and transporters into the model. We then evaluated the predictive performance of the model in patients requiring intensive care using clinical routine data.

RESULTS

Model predictions were considered adequate in healthy subjects and patients with CKD, with predicted-to-observed area under the curve ratios within the two-fold acceptance criterion. Mean prediction error and mean absolute prediction error did not exceed ± 30 and 30%, respectively, except in patients with stage 4 CKD, where they were 70.5 and 75.6%, respectively. The model showed good predictive performance when applied to patients requiring intensive care, but its clinical applicability in this population needs to be further evaluated.

CONCLUSION

We successfully developed whole-body PBPK models to predict cefotaxime pharmacokinetics in different populations. These models represent an additional step toward improving personalized cefotaxime dosing regimens in vulnerable populations.

Static Versus Dynamic Model Predictions of Competitive Inhibitory Metabolic Drug-Drug Interactions via Cytochromes P450: One Step Forward and Two Steps Backwards

BACKGROUND

Predicting metabolic drug-drug interactions (DDIs) via cytochrome P450 enzymes (CYP) is essential in drug development, but controversy has reemerged recently about whether in vitro-in vivo extrapolation (IVIVE) using static models can replace dynamic models for some regulatory filings and label recommendations.

OBJECTIVE

The aim of this study was to determine if static and dynamic models are equivalent for the quantitative prediction of metabolic DDIs arising from competitive CYP inhibition.

METHODS

Drug parameter spaces were varied to simulate 30,000 DDIs between hypothetical substrates and inhibitors of CYP3A4. Predicted area under the plasma concentration-time profile ratios for substrates (AUCr = AUC(presence of precipitant)/AUC(absence of precipitant)) were compared between dynamic simulations (Simcyp® V21) and corresponding static calculations, giving an inter-model discrepancy ratio (IMDR = AUCrdynamic/AUCrstatic). Dynamic simulations were conducted using a 'population' representative and a 'vulnerable patient' representative with maximal concentration (Cmax) or average steady-state concentration (Cavg,ss) as the inhibitor driver concentrations. IMDRs outside the interval 0.8-1.25 were defined as discrepancy between models.

RESULTS

The highest rate of IMDR <0.8 and IMDR >1.25 discrepancies in the 'population' representative was 85.9% and 3.1%, respectively, when using Cavg,ss as the inhibitor driver concentration. Using the 'vulnerable patient' representative showed the highest rate of IMDR >1.25 discrepancies at 37.8%.

CONCLUSION

Static models are not equivalent to dynamic models for predicting metabolic DDIs via competitive CYP inhibition across diverse drug parameter spaces, particularly for vulnerable patients. Caution is warranted in drug development if static IVIVE approaches are used alone to evaluate metabolic DDI risks.

A critical review on approaches to generate and validate virtual population for physiologically based pharmacokinetic models: Methodologies, case studies and way forward

PURPOSE

In silico modeling and simulation techniques such as physiologically based pharmacokinetic (PBPK) and physiologically based biopharmaceutics modeling (PBBM) have demonstrated various applications in drug discovery and development. Virtual bioequivalence leverages these computation tools to predict bioequivalence between reference and test formulations thereby demonstrating possibilities to reduce human studies. A pre-requisite for virtual bioequivalence is development of validated virtual population that depicts the same variability as that of observed in clinic. This development, validation and optimization of virtual population is a key attribute of virtual bioequivalence based on which conclusion of bioequivalence is made.

METHODS

Various strategies for optimization of virtual population based on appropriate considerations of physicochemical, physiological and disposition aspects are demonstrated with the help of six diverse case studies of immediate and modified release formulations. Once the virtual population is optimized to match in vivo variability, it can be used for various applications such as biowaivers, dissolution specification justification, f2 mismatch, establishing dissolution safe space, etc. In this review article, we attempted to describe various methodologies and approaches for optimization of virtual population using Gastroplus.

RESULTS

Strategies based on optimization of virtual population with emphasis on specific and sensitive parameters were portrayed. We have further elucidated considerations related to study design, in vivo variability, sample size for optimization of virtual population from Gastroplus perspective.

CONCLUSION

We believe that this review article provides a step-by-step process for virtual population optimization for interest of biopharmaceutics modeling scientists in order to ensure reliable and credible physiological models.

Computational approach for drug discovery against Gardnerella vaginalis in quest for safer and effective treatments for bacterial vaginosis

Bacterial vaginosis (BV), primarily attributed to Gardnerella vaginalis, poses significant challenges due to antibiotic resistance and suboptimal treatment outcomes. This study presents an integrated approach to identify potential drug targets and screen compounds against this bacterium by leveraging a computational methodology. Subtractive proteomics of the reference strain ASM286196v1/UMB0386 (assembly accession: GCA_002861965.1) facilitated the prioritization of proteins with essential bacterial functions and pathways as potential drug targets. We selected 3-deoxy-7-phosphoheptulonate synthase (aroG gene product; also known as DAHP synthase) for downstream analysis. Molecular docking was employed in PyRx (AutoDock Vina) to predict binding affinities between aroG inhibitors from the ZINC database and 3-deoxy-7-phosphoheptulonate synthase. Molecular dynamics simulations of 100 ns, using GROMACS, validated the stability of drug-target interactions. Additionally, ADMET profiling aided in the selection of compounds with favorable pharmacokinetic properties and safety profile for human hosts. PBPK profiling showed that ZINC98088375 had the highest bioavailability and efficient systemic circulation. Conversely, ZINC5113880 demonstrated the lowest absorption rate (39.661%). Moreover, cirrhosis, steatosis, and renal impairment appeared to influence blood concentration of the drug, impacting bioavailability. The integrative -omics approach utilized in this study underscores the potential of computer-aided drug design and offers a rational strategy for targeted inhibitor discovery against G. vaginalis. The strategy is an attempt to address the limitations of current BV treatments, including antibiotic resistance, and pave way for the development of safer and more effective therapeutics.

A Comprehensive Physiologically Based Pharmacokinetic Model for Predicting Vildagliptin Pharmacokinetics: Insights into Dosing in Renal Impairment

Physiologically based pharmacokinetic (PBPK) modeling is of great importance in the field of medicine. This study aims to construct a PBPK model, which can provide reliable drug pharmacokinetic (PK) predictions in both healthy and chronic kidney disease (CKD) subjects. To do so, firstly a review of the literature was thoroughly conducted and the PK information of vildagliptin was collected. PBPK modeling software, PK-Sim®, was then used to build and assess the IV, oral, and drug-specific models. Next, the average fold error, visual predictive checks, and predicted/observed ratios were used for the assessment of the robustness of the model for all the essential PK parameters. This evaluation demonstrated that all PK parameters were within an acceptable limit of error, i.e., 2 fold. Also to display the influence of CKD on the total and unbound AUC (the area under the plasma concentration-time curve) and to make modifications in dose, the analysis results of the model on this aspect were further examined. This PBPK model has successfully depicted the variations of PK of vildagliptin in healthy subjects and patients with CKD, which can be useful for medical practitioners in dosage optimization in renal disease patients.

Sex as a Biological Variable in Early-Phase Oncology Clinical Trials: Enhancing the Path to Personalised Medicine

Sex is an essential biological variable that influences the development, progression and response to treatment in cancer. Despite this, early-phase cancer clinical trials frequently neglect to consider sex as a variable, creating a barrier to the development of personalised medicine. This article argues that failure to identify and infer sex differences in early-phase clinical trials may result in suboptimal dosing, underestimation of toxicity, and the failure to identify potential sex-specific responses to new systemic anticancer therapies. There should be a greater focus on sex as a biological variable in drug development so that thoughtful and deliberate study design can bring precision to the development of new systemic cancer therapies.

Warfarin-A natural anticoagulant: A review of research trends for precision medication

BACKGROUND

Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability.

PURPOSE

The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage.

METHODS

A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature.

RESULTS

Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database.

CONCLUSION

Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.

Advancements and challenges in pharmacokinetic and pharmacodynamic research on the traditional Chinese medicine saponins: a comprehensive review

Recent research on traditional Chinese medicine (TCM) saponin pharmacokinetics has revealed transformative breakthroughs and challenges. The multicomponent nature of TCM makes it difficult to select representative indicators for pharmacokinetic studies. The clinical application of saponins is limited by their low bioavailability and short half-life, resulting in fluctuating plasma concentrations. Future directions should focus on novel saponin compounds utilizing colon-specific delivery and osmotic pump systems to enhance oral bioavailability. Optimizing drug combinations, such as ginsenosides with aspirin, shows therapeutic potential. Rigorous clinical validation is essential for practical applications. This review emphasizes a transformative era in saponin research, highlighting the need for clinical validation. TCM saponin pharmacokinetics, guided by traditional principles, are in development, utilizing multidisciplinary approaches for a comprehensive understanding. This research provides a theoretical basis for new clinical drugs and supports rational clinical medication.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence

Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to accessible immunotherapy alternatives, with some patients demonstrating innate resistance to these drugs despite past sensitisation to these agents. However, various obstacles stand in the way of this method, most notably the practicality of sequencing each patient's mutanome, selecting immunotherapy targets, and manufacturing specific medications on a large scale. With the robustness and advancement in research techniques, artificial intelligence (AI) is a potential tool that can help refine the mutanome-based immunotherapy for melanoma. Mutanome-based techniques are being employed in the development of immune-stimulating vaccines, improving current options such as adoptive cell treatment, and simplifying immunotherapy responses. Although the use of AI in these approaches is limited by data paucity, cost implications, flaws in AI inference capabilities, and the incapacity of AI to apply data to a broad population, its potential for improving immunotherapy is limitless. Thus, in-depth research on how AI might help the individualisation of immunotherapy utilising knowledge of mutanomes is critical, and this should be at the forefront of melanoma management.

The Physiological and Pharmacological Significance of the Circadian Timing of the HPA Axis: A Mathematical Modeling Approach

As a potent endogenous regulator of homeostasis, the circadian time-keeping system synchronizes internal physiology to periodic changes in the external environment to enhance survival. Adapting endogenous rhythms to the external time is accomplished hierarchically with the central pacemaker located in the suprachiasmatic nucleus (SCN) signaling the hypothalamus-pituitary-adrenal (HPA) axis to release hormones, notably cortisol, which help maintain the body's circadian rhythm. Given the essential role of HPA-releasing hormones in regulating physiological functions, including immune response, cell cycle, and energy metabolism, their daily variation is critical for the proper function of the circadian timing system. In this review, we focus on cortisol and key fundamental properties of the HPA axis and highlight their importance in controlling circadian dynamics. We demonstrate how systems-driven, mathematical modeling of the HPA axis complements experimental findings, enhances our understanding of complex physiological systems, helps predict potential mechanisms of action, and elucidates the consequences of circadian disruption. Finally, we outline the implications of circadian regulation in the context of personalized chronotherapy. Focusing on the chrono-pharmacology of synthetic glucocorticoids, we review the challenges and opportunities associated with moving toward personalized therapies that capitalize on circadian rhythms.

Modeling Red Blood Cell Metabolism in the Omics Era

Red blood cells (RBCs) are abundant (more than 80% of the total cells in the human body), yet relatively simple, as they lack nuclei and organelles, including mitochondria. Since the earliest days of biochemistry, the accessibility of blood and RBCs made them an ideal matrix for the characterization of metabolism. Because of this, investigations into RBC metabolism are of extreme relevance for research and diagnostic purposes in scientific and clinical endeavors. The relative simplicity of RBCs has made them an eligible model for the development of reconstruction maps of eukaryotic cell metabolism since the early days of systems biology. Computational models hold the potential to deepen knowledge of RBC metabolism, but also and foremost to predict in silico RBC metabolic behaviors in response to environmental stimuli. Here, we review now classic concepts on RBC metabolism, prior work in systems biology of unicellular organisms, and how this work paved the way for the development of reconstruction models of RBC metabolism. Translationally, we discuss how the fields of metabolomics and systems biology have generated evidence to advance our understanding of the RBC storage lesion, a process of decline in storage quality that impacts over a hundred million blood units transfused every year.

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

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.

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).

Physiologically-based pharmacokinetic pharmacodynamic parent-metabolite model of edoxaban to predict drug-drug-disease interactions: M4 contribution

This study aimed to develop a physiologically-based pharmacokinetic pharmacodynamic (PBPK/PD) parent-metabolite model of edoxaban, an oral anticoagulant with a narrow therapeutic index, and to predict pharmacokinetic (PK)/PD profiles and potential drug-drug-disease interactions (DDDIs) in patients with renal impairment. A whole-body PBPK model with a linear additive PD model of edoxaban and its active metabolite M4 was developed and validated in SimCYP for healthy adults with or without interacting drugs. The model was extrapolated to situations including renal impairment and drug-drug interactions (DDIs). Observed PK and PD data in adults were compared with predicted data. The effect of several model parameters on the PK/PD response of edoxaban and M4 was investigated in sensitivity analysis. The PBPK/PD model successfully predicted PK profiles of edoxaban and M4 as well as anticoagulation PD responses with or without the influence of interacting drugs. For patients with renal impairment, the PBPK model successfully predicted the fold change in each impairment group. Inhibitory DDI and renal impairment had a synergistic effect on the increased exposure of edoxaban and M4, and their downstream anticoagulation PD effect. Sensitivity analysis and DDDI simulation show that renal clearance, intestinal P-glycoprotein activity, and hepatic OATP1B1 activity are the major factors affecting edoxaban-M4 PK profiles and PD responses. Anticoagulation effect induced by M4 cannot be ignored when OATP1B1 is inhibited or downregulated. Our study provides a reasonable approach to adjust the dose of edoxaban in several complicated scenarios especially when M4 cannot be ignored due to decreased OATP1B1 activity.

Characterization of neonatal and infant enterostomy fluids - Part II: Drug solubility

Previous research revealed marked differences in the composition of intestinal fluids between infants and adults. To explore the impact on the solubilization of orally administered drugs, the present study assessed the solubility of five poorly water-soluble, lipophilic drugs in intestinal fluid pools from 19 infant enterostomy patients (infant HIF). For some but not all drugs, the average solubilizing capacity of infant HIF was similar to that of HIF obtained from adults (adult HIF) in fed conditions. Commonly used fed state simulated intestinal fluids (FeSSIF(-V2)) predicted fairly well drug solubility in the aqueous fraction of infant HIF, but did not account for the substantial solubilization by the lipid phase of infant HIF. Despite similarities in the average solubilities of some drugs in infant HIF and adult HIF or SIF, the underlying solubilization mechanisms likely differ, considering important compositional differences (e.g., low bile salt levels). Finally, the huge variability in composition of infant HIF pools resulted in a highly variable solubilizing capacity, potentially causing variations in drug bioavailability. The current study warrants future research focusing on (i) understanding the mechanisms underlying drug solubilization in infant HIF and (ii) evaluating the sensitivity of oral drug products to interpatient variations in drug solubilization.

Identifying Safety Subgroups at Risk: Assessing the Agreement Between Statistical Alerting and Patient Subgroup Risk

INTRODUCTION

Identifying individual characteristics or underlying conditions linked to adverse drug reactions (ADRs) can help optimise the benefit-risk ratio for individuals. A systematic evaluation of statistical methods to identify subgroups potentially at risk using spontaneous ADR report datasets is lacking.

OBJECTIVES

In this study, we aimed to assess concordance between subgroup disproportionality scores and European Medicines Agency Pharmacovigilance Risk Assessment Committee (PRAC) discussions of potential subgroup risk.

METHODS

The subgroup disproportionality method described by Sandberg et al., and variants, were applied to statistically screen for subgroups at potential increased risk of ADRs, using data from the US FDA Adverse Event Reporting System (FAERS) cumulative from 2004 to quarter 2 2021. The reference set used to assess concordance was manually extracted from PRAC minutes from 2015 to 2019. Mentions of subgroups presenting potential differentiated risk and overlapping with the Sandberg method were included.

RESULTS

Twenty-seven PRAC subgroup examples representing 1719 subgroup drug-event combinations (DECs) in FAERS were included. Using the Sandberg methodology, 2 of the 27 could be detected (one for age and one for sex). No subgroup examples for pregnancy and underlying condition were detected. With a methodological variant, 14 of 27 examples could be detected.

CONCLUSIONS

We observed low concordance between subgroup disproportionality scores and PRAC discussions of potential subgroup risk. Subgroup analyses performed better for age and sex, while for covariates not well-captured in FAERS, such as underlying condition and pregnancy, additional data sources should be considered.

Challenges and Possible Solutions to Direct-Acting Oral Anticoagulants (DOACs) Dosing in Patients with Extreme Bodyweight and Renal Impairment

This article aims to highlight the dosing issues of direct oral anticoagulants (DOACs) in patients with renal impairment and/or obesity in an attempt to develop solutions employing advanced data-driven techniques. DOACs have become widely accepted by clinicians worldwide because of their superior clinical profiles, more predictable pharmacokinetics, and hence more convenient dosing relative to other anticoagulants. However, the optimal dosing of DOACs in extreme bodyweight  patients and patients with renal impairment is difficult to achieve using the conventional dosing approach. The standard dosing approach (fixed-dose) is based on limited data from clinical studies. The existing formulae (models) for determining the appropriate doses for these patient groups leads to suboptimal dosing. This problem of mis-dosing is worsened by the lack of standardized laboratory parameters for monitoring the exposure to DOACs in renal failure and extreme bodyweight patients. Model-informed precision dosing (MIPD) encompasses a range of techniques like machine learning and pharmacometrics modelling, which could uncover key variables and relationships as well as shed more light on the pharmacokinetics and pharmacodynamics of DOACs in patients with extreme bodyweight or renal impairment. Ultimately, this individualized approach-if implemented in clinical practice-could optimise dosing for the DOACs for better safety and efficacy.

Advances in in-vitro bioequivalence testing methods for complex ophthalmic generic products

The United States Food and Drug Administration (USFDA) demands that the generic industry prove topical ocular products' pharmaceutical and bioequivalence (BE). In contrast to generic oral drugs, topical ocular product BE testing has proved difficult. New generic versions are compared to an authorized drug product known as a Reference Listed Drug (RLD) to demonstrate their bioequivalence. If the excellent in-vitro results may support the presumption of equivalence in-vivo performance and the only clinically significant difference between the generic and RLD is in its physicochemical qualities and drug release rate, then in-vivo BE studies may be waived. Proving BE through dissolution tests is a golden standard for most conventional dosage forms. However, due to the limited number of biorelevant in-vitro drug release testing (IVRT) approaches capable of differentiating their performance based on product quality and physicochemical properties, the development of generic ophthalmic products has been slow and time-consuming. Often, BE of topical ophthalmic formulations cannot be proved using a single in-vitro test; therefore, an elaborated discussion on various IVRT methods performed to demonstrate bioequivalence of complex generis like ophthalmic emulsions, suspensions, ointments, and gels is necessary. This manuscript aims to review the status of biowaiver criteria for complex ophthalmic products concerning the product-specific FDA guidance to the generic industry.

Development and Evaluation of a Physiologically Based Pharmacokinetic Model for Predicting Haloperidol Exposure in Healthy and Disease Populations

The physiologically based pharmacokinetic (PBPK) approach can be used to develop mathematical models for predicting the absorption, distribution, metabolism, and elimination (ADME) of administered drugs in virtual human populations. Haloperidol is a typical antipsychotic drug with a narrow therapeutic index and is commonly used in the management of several medical conditions, including psychotic disorders. Due to the large interindividual variability among patients taking haloperidol, it is very likely for them to experience either toxic or subtherapeutic effects. We intend to develop a haloperidol PBPK model for identifying the potential sources of pharmacokinetic (PK) variability after intravenous and oral administration by using the population-based simulator, PK-Sim. The model was initially developed and evaluated to predict the PK of haloperidol and its reduced metabolite in adult healthy population after intravenous and oral administration. After evaluating the developed PBPK model in healthy adults, it was used to predict haloperidol–rifampicin drug–drug interaction and was extended to tuberculosis patients. The model evaluation was performed using visual assessments, prediction error, and mean fold error of the ratio of the observed-to-predicted values of the PK parameters. The predicted PK values were in good agreement with the corresponding reported values. The effects of the pathophysiological changes and enzyme induction associated with tuberculosis and its treatment, respectively, on haloperidol PK, have been predicted precisely. For all clinical scenarios that were evaluated, the predicted values were within the acceptable two-fold error range.

Modelling and Simulation Approaches to Support Formulation Optimization, Clinical Development and Regulatory Assessment of the Topically Applied Formulations- Nimesulide Solution Gel Case Study

The objective of the study was to show how mechanistic modelling can be used to characterize the skin absorption of Nimesulide (NIM) in both in vitro systems and in vivo subjects. A basic PBPK model for oral absorption to characterize the systemic disposition of NIM and MPML MechDermA^TM models for in vitro permeation and in vivo, topical absorption was developed and verified using published data. The developed models utilize drug physicochemical properties, formulation attributes and physiology information either collected from literature and/or from Simcyp databases (systems' data). Following the verification of the PBPK models virtual bioequivalence (VBE) trials were performed both at systemic and local exposure levels (dermis concentrations) to compare these formulations. A parameter sensitivity analysis was conducted to understand the impact of vehicle-related attributes on IVPT (in vitro permeation test) data. The vehicle-stratum corneum lipids partition coefficient in the formulation layer (Kp_{sc_lip:vehicle}) was identified to be an appropriate parameter to take into account the differences in dermal absorption of marketed preparations based on the qualitative composition. Thus, this parameter was optimized for each marketed product based on the published in vitro data. After verification of the IVPT model, IVIVE was performed to assess the predictability of the model for studying the in vivo pharmacokinetics of NIM. The VBE analysis concluded that these formulations are bioequivalent at the level of systemic and local dermis exposure. To summarize, the study shows the use of modelling and simulation (M&S) tools to better understand the behaviour of formulations and their interaction with human physiology.

Safety assessment of natural products in Malaysia: current practices, challenges, and new strategies

The belief that natural products are inherently safe is a primary reason for consumers to choose traditional medicines and herbal supplements for health maintenance and disease prevention. Unfortunately, some natural products on the market have been found to contain toxic compounds, such as heavy metals and microbes, as well as banned ingredients such as aristolochic acids. It shows that the existing regulatory system is inadequate and highlights the importance of thorough safety evaluations. In Malaysia, the National Pharmaceutical Regulatory Agency is responsible for the regulatory control of medicinal products and cosmetics, including natural products. For registration purpose, the safety of natural products is primarily determined through the review of documents, including monographs, research articles and scientific reports. One of the main factors hampering safety evaluations of natural products is the lack of toxicological data from animal studies. However, international regulatory agencies such as the European Food Safety Authority and the United States Food and Drug Administration are beginning to accept data obtained using alternative strategies such as non-animal predictive toxicological tools. Our paper discusses the use of state-of-the-art techniques, including chemometrics, in silico modelling and omics technologies and their applications to the safety assessments of natural products.

Global sensitivity analysis in physiologically-based pharmacokinetic/pharmacodynamic models of inhaled and opioids anesthetics and its application to generate virtual populations

The integration between physiologically-based pharmacokinetics (PBPK) models and pharmacodynamics (PD) models makes it possible to describe the absorption, distribution, metabolism and excretion processes of drugs, together with the concentration-response relationship, being a fundamental framework with wide applications in pharmacology. Nevertheless, the enormous complexity of PBPK models and the large number of parameters that define them leads to the need to study and understand how the uncertainty of the parameters affects the variability of the models output. To study this issue, this paper proposes a global sensitivity analysis (GSA) to identify the parameters that have the greatest influence on the response of the model. It has been selected as study cases the PBPK models of an inhaled anesthetic and an analgesic, along with two PD interaction models that describe two relevant clinical effects, hypnosis and analgesia during general anesthesia. The subset of the most relevant parameters found adequately with the GSA method has been optimized for the generation of a virtual population that represents the theoretical output variability of various model responses. The generated virtual population has the potential to be used for the design, development and evaluation of physiological closed-loop control systems.

Toxicokinetic evaluation during intoxication of psychotropic drugs using brain microdialysis in mice

In the event of an overdose, the pharmacokinetics of the drug may be altered, resulting in an unexpectedly rapid increase in blood and tissue drug concentrations. Because central nervous system (CNS)-acting drugs are the major cause of hospitalization for overdose, brain concentrations, which are closely related to the development of acute psychotropic symptoms, would be important. However, due to the lack of an appropriate model for overdose, it is difficult to predict the CNS symptoms of patients with acute poisoning. To clarify the toxicokinetics during intoxication with CNS-acting drugs, we investigated the relationship between the dose and concentrations in the blood and brain in mice. Therapeutic or toxic doses of phenobarbital, flunitrazepam, imipramine, and amoxapine were administered intraperitoneally to mice. Serum and extracellular fluid of the brain were collected up to 24 hr after administration and analyzed using LC-MS/MS to determine the pharmacokinetic parameters in the serum and brain. A comparison of the four psychotropic drugs showed that the toxicokinetics of amoxapine in the blood and brain are clearly different from others, with the brain concentrations being specifically highly susceptible to increase during dose escalation. These results are consistent with the CNS-related symptoms observed in amoxapine overdose. Therefore, the methodology of the current study could be useful for predicting CNS toxicity during psychotropic drug poisoning.

Two Possible Strategies for Drug Modification of Gemcitabine and Future Contributions to Personalized Medicine

Drug repurposing is an emerging strategy, which uses already approved drugs for new medical indications. One such drug is gemcitabine, an anticancer drug that only works at high doses since a portion is deactivated in the serum, which causes toxicity. In this review, two methods were discussed that could improve the anticancer effect of gemcitabine. The first is a chemical modification by conjugation with cell-penetrating peptides, namely penetratin, pVEC, and different kinds of CPP6, which mostly all showed an increased anticancer effect. The other method is combining gemcitabine with repurposed drugs, namely itraconazole, which also showed great cancer cell inhibition growth. Besides these two strategies, physiologically based pharmacokinetic models (PBPK models) are also the key for predicting drug distribution based on physiological data, which is very important for personalized medicine, so that the correct drug and dosage regimen can be administered according to each patient’s physiology. Taking all of this into consideration, it is believed that gemcitabine can be repurposed to have better anticancer effects.

PBPK Modeling and Simulation and Therapeutic Drug Monitoring: Possible Ways for Antibiotic Dose Adjustment

Pharmacokinetics (PK) is a branch of pharmacology present and of vital importance for the research and development (R&D) of new drugs, post-market monitoring, and continued optimizations in clinical contexts. Ultimately, pharmacokinetics can contribute to improving patients’ clinical outcomes, helping enhance the efficacy of treatments, and reducing possible adverse side effects while also contributing to precision medicine. This article discusses the methods used to predict and study human pharmacokinetics and their evolution to the current physiologically based pharmacokinetic (PBPK) modeling and simulation methods. The importance of therapeutic drug monitoring (TDM) and PBPK as valuable tools for Model-Informed Precision Dosing (MIPD) are highlighted, with particular emphasis on antibiotic therapy since dosage adjustment of antibiotics can be vital to ensure successful clinical outcomes and to prevent the spread of resistant bacterial strains.

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

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp^® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.

Challenges Faced by Clinicians in the Personalized Treatment Planning: A Literature Review and the First Results of the Russian National Cancer Program

Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients' cancer treatment and some challenges that this field faces now in Russia. In this report, we consider the key characteristics for planning an optimal clinical treatment regimen and which should be included in the algorithm of clinical decision support systems. We discuss the perspectives of implementing artificial intelligence-based systems in cancer treatment planning in Russia.

Let's talk about sex: Differences in drug therapy in males and females

Professor Henry Higgins in My Fair Lady said, 'Why can't a woman be more like a man?' Perhaps unintended, such narration extends to the reality of current drug development. A clear sex-gap exists in pharmaceutical research spanning from preclinical studies, clinical trials to post-marketing surveillance with a bias towards males. Consequently, women experience adverse drug reactions from approved drug products more often than men. Distinct differences in pharmaceutical response across drug classes and the lack of understanding of disease pathophysiology also exists between the sexes, often leading to suboptimal drug therapy in women. This review explores the influence of sex as a biological variable in drug delivery, pharmacokinetic response and overall efficacy in the context of pharmaceutical research and practice in the clinic. Prospective recommendations are provided to guide researchers towards the consideration of sex differences in methodologies and analyses. The promotion of disaggregating data according to sex to strengthen scientific rigour, encouraging innovation through the personalisation of medicines and adopting machine learning algorithms is vital for optimised drug development in the sexes and population health equity.

Prediction of Free Drug Absorption in Cyclodextrin Formulation by a Modified Physiologically Based Pharmacokinetic Model and Phase Solubility 3-D Surface Graph

PURPOSE

Cyclodextrin (CD) is commonly used to enhance the solubility of oral drugs. However, with the increase of CD concentrations, the fraction of free drug molecules decreases, which may potentially impede drug absorption. This study aims to predict the optimal ratio between drug and CD to achieve the best absorption efficiency by computational simulation.

METHODS

First, a physiologically based pharmacokinetic (PBPK) model was developed. This model can continuously adjust absorption according to free drug fraction and was validated against two model drugs, progesterone (PG) and andrographolide (AG). The further analysis involves 3-D surface graphs to investigate the relationship between free drug amount, theoretically absorbable concentration, and contents of drug and CD in the formulation.

RESULTS

The PBPK model predicted the PK behavior of two drugs well. The concentration ratio of drug to CD, leading to maximal free drug amount and the best absorption efficiency, is nearly the same as the slope determined in the phase solubility test. The new modified PBPK model and 3-D surface graph can easily predict the absorption difference of formulations with various drug/CD ratios.

CONCLUSION

This PBPK model and 3-D surface graph can predict the absorption and determine the optimal concentration ratio of CD formulation, which could accelerate the R&D of CD formulation.

Determination of Verapamil Hydrochloride and Norverapamil Hydrochloride in Rat Plasma by Capillary Electrophoresis With End-Column Electrochemiluminescence Detection and Their Pharmacokinetics Study

In this study, we developed a new method for simultaneous determination of verapamil hydrochloride (VerHCl) and its metabolite norverapamil hydrochloride (NorHCl) by using the capillary electrophoresis-electrochemiluminescence. Under optimized experimental conditions, the linear ranges of the VerHCl and NorHCl concentrations were 0.015-10.0 and 0.060-10.0 μg/mL, respectively. The linearity relations were determined using the respective regression equations y = 581.2x + 19.94 and y = 339.4x + 29.16. The respective limits of detection (S/N = 3) were 0.006 and 0.024 μg/mL. The proposed method was used to study the pharmacokinetics of both agents in rat plasma. The maximum concentration (Cmax), half-life time (T1/2) and time to peak (Tmax) were 683.21 ± 74.81 ng/mL, 0.52 ± 0.21 h and 2.49 ± 0.32 h for VerHCl and 698.42 ± 71.45 ng/mL, 1.14 ± 0.26 h and 2.83 ± 0.23 h for NorHCl, respectively, following oral administration of 10 mg/kg VerHCl.

A physiologically based pharmacokinetic analysis to predict the pharmacokinetics of intravenous isavuconazole in patients with or without hepatic impairment

Isavuconazole (ISA) is an azole antifungal used in the treatment of invasive aspergillosis and mucormycosis. Patients with mild and moderate hepatic impairment have lower clearance (CL) as compared to the healthy population. Currently, there is no data on ISA in patients with severe hepatic impairment (Child-Pugh Class C). The purpose of this study was to build a physiologically based pharmacokinetic (PBPK) model to describe the pharmacokinetics (PK) of intravenous ISA, and to predict changes in ISA disposition in different patient populations and in patients with hepatic impairment to guide personalized dosing. By incorporating the systemic and drug specific parameters of ISA, the model was initially developed in healthy population and validated with 10 independent PK profiles obtained from healthy subjects and from patients with normal liver function. The results showed a satisfactory predictive capacity, with most of the relative predictive errors being between ±30% for area under the curve (AUC) and C_max The observed plasma concentration-time profiles of ISA were well described by the model predicted profiles. The model adequately predicted the reduced CL of ISA in patients with mild and moderate hepatic impairment. Furthermore, the model predicted a decrease in CL of about 60% in patients with severe hepatic impairment. Therefore, we recommend reducing the dose by 50% in patients with severe hepatic impairment. The model also predicted differences in the PK of ISA between Caucasian and Asian population, with the CL ratio of 0.67 in Chinese vs Caucasian population. The developed PBPK model of ISA provides a reasonable approach for optimizing the dosage regimen in different ethnic populations and in patients with severe hepatic impairment.

Circadian Effects of Drug Responses

Circadian rhythms describe physiological systems that repeat themselves with a cycle of approximately 24 h. Our understanding of the cellular and molecular origins of these oscillations has improved dramatically, allowing us to appreciate the significant role these oscillations play in maintaining physiological homeostasis. Circadian rhythms allow living organisms to predict and efficiently respond to a dynamically changing environment, set by repetitive day/night cycles. Since circadian rhythms underlie almost every aspect of human physiology, it is unsurprising that they also influence the response of a living organism to disease, stress, and therapeutics. Therefore, not only do the mechanisms that maintain health and disrupt homeostasis depend on our internal circadian clock, but also the way drugs are perceived and function depends on these physiological rhythms. We present a holistic view of the therapeutic process, discussing components such as disease state, pharmacokinetics, and pharmacodynamics, as well as adverse reactions that are critically affected by circadian rhythms. We outline challenges and opportunities in moving toward personalized medicine approaches that explore and capitalize on circadian rhythms for the benefit of the patient.

Evaluation of Pharmacokinetic Drug-Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches

Pharmacokinetic drug–drug interactions (DDIs) occur when a drug alters the absorption, transport, distribution, metabolism or excretion of a co-administered agent. The occurrence of pharmacokinetic DDIs may result in the increase or the decrease of drug concentrations, which can significantly affect the drug efficacy and safety in patients. Enzyme-mediated DDIs are of primary concern, while the transporter-mediated DDIs are less understood but also important. In this review, we presented an overview of the different mechanisms leading to DDIs, the in vitro experimental tools for capturing the factors affecting DDIs, and in silico methods for quantitative predictions of DDIs. We also emphasized the power and strategy of physiologically based pharmacokinetic (PBPK) models for the assessment of DDIs, which can integrate relevant in vitro data to simulate potential drug interaction in vivo. Lastly, we pointed out the future directions and challenges for the evaluation of pharmacokinetic DDIs.

The emerging role of physiologically-based pharmacokinetic/biopharmaceutics modeling in formulation development

Computer-based (in silico) modeling & simulation tools have been embraced in different fields of pharmaceutics for a variety of applications. Among these, physiologically-based pharmacokinetic/biopharmaceutics modeling (PBPK/PBBM) emerged as a particularly useful tool in formulation development. PBPK/PBBM facilitated strategies have been increasingly evaluated over the past few years, as demonstrated by several reports from the pharmaceutical industry, and a number of research and review papers on this subject. Also, the leading regulatory authorities have recently issued guidance on the use of PBPK modeling in formulation design. In silico PBPK models can comprise different dosing routes (oral, intraoral, parenteral, inhalation, ocular, dermal etc.), although the majority of published examples refer to modeling of oral drugs performance. In order to facilitate the use of PBPK modeling tools, a couple of companies have launched commercially available software such as GastroPlus™, Simcyp™ PBPK Simulator and PK-Sim®. This paper highlights various application fields of PBPK/PBBM modeling, along with the basic principles, advantages and limitations of this approach, and provides relevant examples to demonstrate the practical utility of modeling & simulation tools in different stages of formulation development.

Dose Optimization in Kidney Disease: Opportunities for PBPK Modeling and Simulation

Kidney disease affects pharmacokinetic (PK) profiles of not only renally cleared drugs but also nonrenally cleared drugs. The impact of kidney disease on drug disposition has not been fully elucidated, but describing the extent of such impact is essential for conducting dose optimization in kidney disease. Accurate evaluation of kidney function has been a clinical interest for dose optimization, and more scientists pay attention and conduct research for clarifying the role of drug transporters, metabolic enzymes, and their interplay in drug disposition as kidney disease progresses. Physiologically based pharmacokinetic (PBPK) modeling and simulation can provide valuable insights for dose optimization in kidney disease. It is a powerful tool to integrate discrete knowledge from preclinical and clinical research and mechanistically investigate system- and drug-dependent factors that may contribute to the changes in PK profiles. PBPK-based prediction of drug exposures may be used a priori to adjust dosing regimens and thereby minimize the likelihood of drug-related toxicity. With real-time clinical studies, parameter estimation may be performed with PBPK approaches that can facilitate identification of sources of interindividual variability. PBPK modeling may also facilitate biomarker research that aids dose optimization in kidney disease. U.S. Food and Drug Administration guidances related to conduction of PK studies in kidney impairment and PBPK documentation provide the foundation for facilitating model-based dose-finding research in kidney disease.

Predicting topical drug clearance from the skin

For topical drug products that target sites of action in the viable epidermal and/or upper dermal compartment of the skin, the local concentration profiles have proven difficult to quantify because drug clearance from the viable cutaneous tissue is not well characterised. Without such knowledge, of course, it is difficult-if not impossible-to predict a priori whether and over what time frame a topical formulation will permit an effective concentration of drug within the skin 'compartment' to be achieved. Here, we test the hypothesis that valuable information about drug disposition, and specifically its clearance, in this experimentally difficult-to-access compartment (at least, in vivo) can be derived from available systemic pharmacokinetic data for drugs administered via transdermal delivery systems. A multiple regression analysis was undertaken to determine the best-fit empirical correlation relating clearance from the skin to known or easily calculable drug properties. It was possible, in this way, to demonstrate a clear relationship between drug clearance from the skin and key physical chemical properties of the drug (molecular weight, log P and topological polar surface area). It was further demonstrated that values predicted by the model correlated well with those derived from in vitro skin experiments.

Alterations in drug disposition in older adults: a focus on geriatric syndromes

INTRODUCTION

Age-associated physiological changes can alter the disposition of drugs, however, pathophysiological changes associated with geriatric syndromes in older adults may lead to even greater heterogeneity in pharmacokinetics. Geriatric syndromes are common health problems in older adults which have multifactorial causes and do not fit into distinct organ-based disease categories. With older adults being the greatest users of medications, understanding both age- and geriatric syndrome-related changes is important clinically to ensure safe and effective medication use.

AREAS COVERED

This review provides an overview of current evidence regarding pharmacokinetic alterations that occur with aging and in common geriatric syndromes, including frailty, sarcopenia, dementia, polypharmacy and enteral feeding. The evidence is presented according to the four primary pharmacokinetic processes (Absorption, Distribution, Metabolism and Excretion).

EXPERT OPINION

There is some evidence to inform our understanding of the impact of chronological aging and various geriatric syndromes on drug disposition. However, many areas require more research, including drug induced inhibition and induction of cytochrome P450 enzymes and the clinical utility of emerging methods for estimating renal function. There is a need to develop tools to predict alterations in drug disposition in subgroups of older adults, particularly where the currently available clinical information is sparse.

Saliva versus Plasma Therapeutic Drug Monitoring of Gentamicin in Jordanian Preterm Infants. Development of a Physiologically-Based Pharmacokinetic (PBPK) Model and Validation of Class II Drugs of Salivary Excretion Classification System

Gentamicin has proven to be a very successful treatment for bacterial infection, but it also can cause adverse effects, especially ototoxicity, which is irreversible. Therapeutic drug monitoring (TDM) in saliva is a more convenient non-invasive alternative compared to plasma. A physiologically-based pharmacokinetic (PBPK) model of gentamicin was built and validated using previously-published plasma and saliva data. The validated model was then used to predict experimentally-observed plasma and saliva gentamicin TDM data in Jordanian pediatric preterm infant patients measured using sensitive LCMS/MS method. A correlation was established between plasma and saliva exposures. The developed PBPK model predicted previously reported gentamicin levels in plasma, saliva and those observed in the current study. A good correlation was found between plasma and saliva exposures. The PBPK model predicted that gentamicin in saliva is 5-7 times that in plasma, which is in agreement with observed results. Saliva can be used as an alternative for TDM of gentamicin in preterm infant patients. Exposure to gentamicin in plasma and saliva can reliably be predicted using the developed PBPK model in patients.

Can Population Pharmacokinetics of Antibiotics be Extrapolated? Implications of External Evaluations

BACKGROUND AND OBJECTIVE

External evaluation is an important issue in the population pharmacokinetic analysis of antibiotics. The purpose of this review was to summarize the current approaches and status of external evaluations and discuss the implications of external evaluation results for the future individualization of dosing regimens.

METHODS

We systematically searched the PubMed and EMBASE databases for external evaluation studies of population analysis and extracted the relevant information from these articles. A total of 32 studies were included in this review.

RESULTS

Vancomycin was investigated in 17 (53.1%) articles and was the most studied drug. Other studied drugs included gentamicin, tobramycin, amikacin, amoxicillin, ceftaroline, meropenem, fluconazole, voriconazole, and rifampicin. Nine (28.1%) studies were prospective, and the sample size varied widely between studies. Thirteen (40.6%) studies evaluated the population pharmacokinetic models by systematically searching for previous studies. Seven (21.9%) studies were multicenter studies, and 27 (84.4%) adopted the sparse sampling strategy. Almost all external evaluation studies of antibiotics (93.8%) used metrics for prediction-based diagnostics, while relatively fewer studies were based on simulations (46.9%) and Bayesian forecasting (25.0%).

CONCLUSION

The results of external evaluations in previous studies revealed the poor extrapolation performance of existing models of prediction- and simulation-based diagnostics, whereas the posterior Bayesian method could improve predictive performance. There is an urgent need for the development of standards and guidelines for external evaluation studies.

Evaluation of the Impacts of Formulation Parameters on the Pharmacokinetics and Bioequivalence of Risperidone Orodispersible Film: a Physiologically Based Pharmacokinetic Modeling Approach

The purpose of this study was to investigate the impacts of the formulation parameters on the pharmacokinetics and bioequivalence of risperidone orodispersible film (ODF) using physiologically based pharmacokinetic model. The pharmacokinetic profiles of two risperidone ODFs, which exhibit different in vitro dissolution, were examined in Beagle dogs after supralingual administration. Subsequently, a physiologically based pharmacokinetic (PBPK) model was constructed to evaluate the in vivo performance of risperidone ODF. The parameter sensitivity analysis (PSA) was used to access the impacts of formulation parameters on the pharmacokinetics of risperidone. Moreover, the validated PBPK model was applied to predict human pharmacokinetic profiles and examine the bioequivalence of these two ODFs. These two ODFs displayed similar risperidone pharmacokinetic profiles in dogs. The parameter sensitivity analysis indicated that the changes in the solubility, particle size, particle density, and diffusion coefficient did not have obvious influence on the in vivo properties of risperidone ODF. Alternation of the in vitro complete dissolution time in water from 15 to 30 min led to a 30% decrease in C_max and 20% of increase in T_max. AUC_0-∞ would be decreased if risperidone was not fully released within 1 h. As both ODFs completely released risperidone within 15 min, the difference in the extent of in vivo absorption, intestinal regional absorption location, and plasma concentration-time curves between these two ODFs was almost negligible. Consequently, a bioequivalence was foreseen in humans. The in vitro cumulative dissolution percentage in water at 15 min was found to be the major determinant on the in vivo properties of risperidone ODF. PBPK modeling appears to be an innovative strategy to guide the development of risperidone ODF.

PBPK modeling of impact of nonalcoholic fatty liver disease on toxicokinetics of perchloroethylene in mice

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD), a major cause of chronic liver disease in the Western countries with increasing prevalence worldwide, may substantially affect chemical toxicokinetics and thereby modulate chemical toxicity.

OBJECTIVES

This study aims to use physiologically-based pharmacokinetic (PBPK) modeling to characterize the impact of NAFLD on toxicokinetics of perchloroethylene (perc).

METHODS

Quantitative measures of physiological and biochemical changes associated with the presence of NAFLD induced by high-fat or methionine/choline-deficient diets in C57B1/6 J mice are incorporated into a previously developed PBPK model for perc and its oxidative and conjugative metabolites. Impacts on liver fat and volume, as well as blood:air and liver:air partition coefficients, are incorporated into the model. Hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation is conducted to characterize uncertainty, as well as disease-induced variability in toxicokinetics.

RESULTS

NAFLD has a major effect on toxicokinetics of perc, with greater oxidative and lower conjugative metabolism as compared to healthy mice. The NAFLD-updated PBPK model accurately predicts in vivo metabolism of perc through oxidative and conjugative pathways in all tissues across disease states and strains, but underestimated parent compound concentrations in blood and liver of NAFLD mice.

CONCLUSIONS

We demonstrate the application of PBPK modeling to predict the effects of pre-existing disease conditions as a variability factor in perc metabolism. These results suggest that non-genetic factors such as diet and pre-existing disease can be as influential as genetic factors in altering toxicokinetics of perc, and thus are likely contribute substantially to population variation in its adverse effects.