Physiologically-based pharmacokinetics in drug development and regulatory science

Exploring the Impact of Pharmacological Target-Mediated Low Plasma Exposure in Lead Compound Selection in Drug Discovery - A Modeling Approach

Small-molecule drug development faces the challenge of low success rate. In this paper, we propose one potential cause that may occur in the preclinical phase and has rarely been brought up before - the neglected target-mediated low plasma exposure, and the subsequent lead compound mis-selection due to conventional pharmacokinetic criteria requiring sufficient plasma exposure and desired half-life. To evaluate the concept of target-mediate low plasma exposure, we established a minimal physiologically-based pharmacokinetic (mPBPK) model to evaluate the concentration-time profiles of a group of virtual lead series analogs in plasma and in tissues with and without pharmacological target expression. Simulation results demonstrated that the candidate with the highest target binding has the lowest plasma exposure due to target-mediated tissue retention. The traditional PK criteria, such as the requirement of sufficient plasma exposure and desired half-life, may potentially result in lead compound mis-selection by discarding the appropriate and best candidate(s). The mPBPK model was partially validated using 4 tyrosine kinase inhibitors based on our in-house PK and tissue distribution data obtained in animals. The association rate constant (Kass) was estimated to be 49.8 h-1, 31.4 h-1, 8.58 h-1, and 1.91 h-1 for afatinib, dasatinib, gefitinib, and sorafenib, respectively. Among these four model drugs, a strong correlation was observed between their Kass values and AUChigh-perfused tissue /AUCplasma ratios, a metric of tissue retention. Our mPBPK modeling and simulation results indicated that the concept of target-mediated low plasma exposure should be kept in mind during the lead compound selection process.

A Minimal PBPK Model Describes the Differential Disposition of Silica Nanoparticles In Vivo

Nanoparticles (NPs) have emerged as promising candidates for drug delivery due to their tunable physical and chemical properties. Among these, silica nanoparticles (SiNPs) are particularly valued for their biocompatibility and adaptability in applications like drug delivery and medical imaging. However, predicting SiNP biodistribution and clearance remains a significant challenge. To address this, we developed a minimal physiologically-based pharmacokinetic (mPBPK) model to simulate the systemic disposition of SiNPs, calibrated using in vivo PK data from mice. The model assesses how variations in surface charge, size, porosity, and geometry influence SiNP biodistribution across key organs, including the kidneys, lungs, liver, and spleen. A global sensitivity analysis identified the most influential parameters, with the unbound fraction and elimination rate constants for the kidneys and MPS emerging as critical determinants of SiNP clearance. Non-compartmental analysis (NCA) further revealed that aminated SiNPs exhibit high accumulation in the liver, spleen, and kidneys, while mesoporous SiNPs primarily accumulate in the lungs. Rod-shaped SiNPs showed faster clearance compared to spherical NPs. The mPBPK model was extrapolated to predict SiNP behavior in humans, yielding strong predictive accuracy with Pearson correlation coefficients of 0.98 for mice and 0.92 for humans. This model provides a robust framework for predicting the pharmacokinetics of diverse SiNPs, offering valuable insights for optimizing NP-based drug delivery systems and guiding the translation of these therapies from preclinical models to human applications.

Physiologically based pharmacokinetic models for predicting lamotrigine exposure and dose optimization in pediatric patients receiving combination therapy with carbamazepine or valproic acid

INTRODUCTION

Lamotrigine (LTG) is an antiepileptic drug that has been used in pediatric epilepsy as a combination therapy or monotherapy after stabilization in recent years. However, there are significant drug-drug interactions (DDI) between LTG and combined drugs such as carbamazepine (CBZ) and valproic acid (VPA). It is particularly important to consider the risk of DDI in combination therapy for intractable epilepsy in pediatric patients. Therefore, it is necessary to adjust the dosage of LTG accordingly. The aim of this study was to establish and validate a pediatric physiologically based pharmacokinetic (PBPK) model for predicting LTG exposure. The model is designed to explore the potential for quantifying pharmacokinetic (PK) DDI of LTG when administered concurrently with CBZ or VPA in pediatric patients.

METHOD

Adult and pediatric PBPK models for LTG and VPA were developed using PK-Sim® software in combination with physiological information and drug-specific parameters, and a DDI model was developed in combination with the published CBZ model. The models were validated against available PK data.

RESULTS

Predictive and observational results in adults, children, and the DDI model were in good agreement. The recommended doses of LTG for preschool children (2-6 years) and school-aged children (6-12 years) in the absence of drug interactions were 1.47 and 1.2 times higher than those for adults, respectively; 3.1 and 2.6 times higher than those for adults in combination with CBZ; and 0.67 and 0.57 times lower than those for adults in combination with VPA. In addition, plasma exposures in adolescents (12-18 years) were similar to those in adults at the same doses.

CONCLUSION

We have successfully developed PBPK models and DDI models for LTG in adults and children, which provide a reference for rational drug use in the pediatric population.

Mathematical Modeling of the Gastrointestinal System for Preliminary Drug Absorption Assessment

The objective of this study is to demonstrate the potential of a multicompartmental mathematical model to simulate the activity of the gastrointestinal system after the intake of drugs, with a limited number of parameters. The gastrointestinal system is divided into five compartments, modeled as both continuous systems with discrete events (stomach and duodenum) and systems with delay (jejunum, ileum, and colon). The dissolution of the drug tablet occurs in the stomach and is described through the Noyes-Whitney equation, with pH dependence expressed through the Henderson-Hasselbach relationship. The boluses resulting from duodenal activity enter the jejunum, ileum, and colon compartments, where drug absorption takes place as blood flows countercurrent. The model includes only three parameters with assigned physiological meanings. It was tested and validated using data from in vivo experiments. Specifically, the model was tested with the concentration profiles of nine different drugs and validated using data from two drugs with varying initial concentrations. Overall, the outputs of the model are in good agreement with experimental data, particularly with regard to the time of peak concentration. The primary sources of discrepancy were identified in the concentration decay. The model's main strength is its relatively low computational cost, making it a potentially excellent tool for in silico assessment and prediction of drug adsorption in the intestine.

Drug-Drug Interactions between COVID-19 and Tuberculosis Medications: A Comprehensive Review of CYP450 and Transporter-Mediated Effects

Most medications undergo metabolism and elimination via CYP450 enzymes, while uptake and efflux transporters play vital roles in drug elimination from various organs. Interactions often occur when multiple drugs share CYP450-transporter-mediated metabolic pathways, necessitating a unique clinical care strategy to address the diverse types of CYP450 and transporter-mediated drug-drug interactions (DDI). The primary focus of this review is to record relevant mechanisms regarding DDI between COVID-19 and tuberculosis (TB) treatments, specifically through the influence of CYP450 enzymes and transporters on drug absorption, distribution, metabolism, elimination, and pharmacokinetics. This understanding empowers clinicians to prevent subtherapeutic and supratherapeutic drug levels of COVID medications when co-administered with TB drugs, thereby mitigating potential challenges and ensuring optimal treatment outcomes. A comprehensive analysis is presented, encompassing various illustrative instances of TB drugs that may impact COVID-19 clinical behavior, and vice versa. This review aims to provide valuable insights to healthcare providers, facilitating informed decision-making and enhancing patient safety while managing co-infections. Ultimately, this study contributes to the body of knowledge necessary to optimize therapeutic approaches and improve patient outcomes in the face of the growing challenges posed by infectious diseases.

Extended Pharmacokinetics Improve Site-Specific Prodrug Activation Using Radiation

Radiotherapy is commonly used to treat cancer, and localized energy deposited by radiotherapy has the potential to chemically uncage prodrugs; however, it has been challenging to demonstrate prodrug activation that is both sustained in vivo and truly localized to tumors without affecting off-target tissues. To address this, we developed a series of novel phenyl-azide-caged, radiation-activated chemotherapy drug-conjugates alongside a computational framework for understanding corresponding pharmacokinetic and pharmacodynamic (PK/PD) behaviors. We especially focused on an albumin-bound prodrug of monomethyl auristatin E (MMAE) and found it blocked tumor growth in mice, delivered a 130-fold greater amount of activated drug to irradiated tumor versus unirradiated tissue, was 7.5-fold more efficient than a non albumin-bound prodrug, and showed no appreciable toxicity compared to free or cathepsin-activatable drugs. These data guided computational modeling of drug action, which indicated that extended pharmacokinetics can improve localized and cumulative drug activation, especially for payloads with low vascular permeability and diffusivity and particularly in patients receiving daily treatments of conventional radiotherapy for weeks. This work thus offers a quantitative PK/PD framework and proof-of-principle experimental demonstration of how extending prodrug circulation can improve its localized activity in vivo.

Distribution Clearance: Significance and Underlying Mechanisms

PURPOSE

Evaluation of distribution kinetics is a neglected aspect of pharmacokinetics. This study examines the utility of the model-independent parameter whole body distribution clearance (CLD) in this respect.

METHODS

Since mammillary compartmental models are widely used, CLD was calculated in terms of parameters of this model for 15 drugs. The underlying distribution processes were explored by assessment of relationships to pharmacokinetic parameters and covariates.

RESULTS

The model-independence of the definition of the parameter CLD allowed a comparison of distributional properties of different drugs and provided physiological insight. Significant changes in CLD were observed as a result of drug-drug interactions, transporter polymorphisms and a diseased state.

CONCLUSION

Total distribution clearance CLD is a useful parameter to evaluate distribution kinetics of drugs. Its estimation as an adjunct to the model-independent parameters clearance and steady-state volume of distribution is advocated.

Gastroretentive fibrous dosage forms for prolonged delivery of sparingly-soluble tyrosine kinase inhibitors. Part 3: Theoretical models of drug concentration in blood

In this part, drug concentration in blood after ingesting slow-release gastroretentive fibrous dosage forms and immediate-release particulate forms is modeled. The tyrosine kinase inhibitor nilotinib, which is slightly soluble in low-pH gastric fluid but practically insoluble in pH-neutral intestinal fluid is used as drug. The models suggest that upon ingestion, the fibrous dosage form expands, is retained in the stomach for prolonged time, and releases drug into the gastric fluid at a constant rate. The released drug molecules flow into the duodenum with the gastric fluid, and are absorbed by the blood. The drug is eliminated from the blood by the liver at a rate proportional to its concentration. Eventually, the elimination and absorption rates will be equal, and the drug concentration in blood plateaus out. After the gastric residence time drug absorption stops, and the drug concentration in blood drops to zero. By contrast, after administering an immediate-release particulate dosage form the drug particles are swept out of the stomach rapidly, and drug absorption stops much earlier. The drug concentration in blood rises and falls without attaining steady state. The gastroretentive fibrous dosage forms enable a constant drug concentration in blood for drugs that are insoluble in intestinal fluids.

Investigating clinical pharmacokinetics of brivaracetam by using a pharmacokinetic modeling approach

The development of technology and the processing speed of computing machines have facilitated the evaluation of advanced pharmacokinetic (PK) models, making modeling processes simple and faster. The present model aims to analyze the PK of brivaracetam (BRV) in healthy and diseased populations. A comprehensive literature review was conducted to incorporate the BRV plasma concentration data and its input parameters into PK-Sim software, leading to the creation of intravenous (IV) and oral models for both populations. The developed physiologically based pharmacokinetic (PBPK) model of BRV was then assessed using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for PK parameters including the maximum systemic concentration (Cmax), the area under the curve at time 0 to t (AUC0-∞), and drug clearance (CL). The PBPK model of BRV demonstrated that mean Robs/pre ratios of the PK parameters remained within the acceptable limits when assessed against a twofold error margin. Furthermore, model predictions were carried out to assess how AUC0-∞ is affected following the administration of BRV in individuals with varying degrees of liver cirrhosis, ranging from different child-pugh (CP) scores like A, B, and C. Moreover, dose adjustments were recommended by considering the variations in Cmax and CL in various kidney disease stages (mild to severe).

A physiologically-based pharmacokinetic/pharmacodynamic modeling approach for drug-drug-gene interaction evaluation of S-warfarin with fluconazole

Warfarin is a widely used anticoagulant, and its S-enantiomer has higher potency compared to the R-enantiomer. S-warfarin is mainly metabolized by cytochrome P450 (CYP) 2C9, and its pharmacological target is vitamin K epoxide reductase complex subunit 1 (VKORC1). Both CYP2C9 and VKORC1 have genetic polymorphisms, leading to large variations in the pharmacokinetics (PKs) and pharmacodynamics (PDs) of warfarin in the population. This makes dosage management of warfarin difficult, especially in the case of drug-drug interactions (DDIs). This study provides a whole-body physiologically-based pharmacokinetic/PD (PBPK/PD) model of S-warfarin for predicting the effects of drug-drug-gene interactions on S-warfarin PKs and PDs. The PBPK/PD model of S-warfarin was developed in PK-Sim and MoBi. Drug-dependent parameters were obtained from the literature or optimized. Of the 34 S-warfarin plasma concentration-time profiles used, 96% predicted plasma concentrations within twofold range compared to observed data. For S-warfarin plasma concentration-time profiles with CYP2C9 genotype, 364 of 386 predicted plasma concentration values (~94%) fell within the twofold of the observed values. This model was tested in DDI predictions with fluconazole as CYP2C9 perpetrators, with all predicted DDI area under the plasma concentration-time curve to the last measurable timepoint (AUClast) ratio within twofold of the observed values. The anticoagulant effect of S-warfarin was described using an indirect response model, with all predicted international normalized ratio (INR) within twofold of the observed values. This model also incorporates a dose-adjustment method that can be used for dose adjustment and predict INR when warfarin is used in combination with CYP2C9 perpetrators.

A physiologically based pharmacokinetic/pharmacodynamic model to determine dosage regimens and withdrawal intervals of aditoprim against Streptococcus suis

Introduction: Streptococcus suis (S. suis) is a zoonotic pathogen threatening public health. Aditoprim (ADP), a novel veterinary medicine, exhibits an antibacterial effect against S. suis. In this study, a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model was used to determine the dosage regimens of ADP against S. suis and withdrawal intervals. Methods: The PBPK model of ADP injection can predict drug concentrations in plasma, liver, kidney, muscle, and fat. A semi-mechanistic pharmacodynamic (PD) model, including susceptible subpopulation and resistant subpopulation, is successfully developed by a nonlinear mixed-effect model to evaluate antibacterial effects. An integrated PBPK/PD model is conducted to predict the time-course of bacterial count change and resistance development under different ADP dosages. Results: ADP injection, administrated at 20 mg/kg with 12 intervals for 3 consecutive days, can exert an excellent antibacterial effect while avoiding resistance emergence. The withdrawal interval at the recommended dosage regimen is determined as 18 days to ensure food safety. Discussion: This study suggests that the PBPK/PD model can be applied as an effective tool for the antibacterial effect and safety evaluation of novel veterinary drugs.

Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023)

PURPOSE

The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis.

METHODS

We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses.

RESULTS

A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field.

CONCLUSION

We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.

Can 3D Printed Tablets Be Bioequivalent and How to Test It: A PBPK Model Based Virtual Bioequivalence Study for Ropinirole Modified Release Tablets

As the field of personalized dosing develops, the pharmaceutical manufacturing industry needs to offer flexibility in terms of tailoring the drug release and strength to the individual patient's needs. One of the promising tools which have such capacity is 3D printing technology. However, manufacturing small batches of drugs for each patient might lead to huge test burden, including the need to conduct bioequivalence trials of formulations to support the change of equipment or strength. In this paper we demonstrate how to use 3D printing in conjunction with virtual bioequivalence trials based on physiologically based pharmacokinetic (PBPK) modeling. For this purpose, we developed 3D printed ropinirole formulations and tested their bioequivalence with the reference product Polpix. The Simcyp simulator and previously developed ropinirole PBPK model were used for the clinical trial simulations. The Weibull-fitted dissolution profiles of test and reference formulations were used as inputs for the model. The virtual bioequivalence trials were run using parallel design. The study power of 80% was reached using 125 individuals. The study demonstrated how to use PBPK modeling in conjunction with 3D printing to test the virtual bioequivalence of newly developed formulations. This virtual experiment demonstrated the bioequivalence of one of the newly developed formulations with a reference product available on a market.

A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat

An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893-4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.

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.

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.

A weight of evidence approach for bioaccumulation assessment

Bioaccumulation assessments conducted by regulatory agencies worldwide use a variety of methods, types of data, metrics, and categorization criteria. Lines of evidence (LoE) for bioaccumulation assessment can include bioaccumulation metrics such as in vivo bioconcentration factor (BCF) and biomagnification factor (BMF) data measured from standardized laboratory experiments, and field (monitoring) data such as BMFs, bioaccumulation factors (BAFs), and trophic magnification factors (TMFs). In silico predictions from mass-balance models and quantitative structure-activity relationships (QSARs) and a combination of in vitro biotransformation rates and in vitro-in vivo extrapolation (IVIVE) models can also be used. The myriad bioaccumulation metrics and categorization criteria and underlying uncertainty in measured or modeled data can make decision-making challenging. A weight of evidence (WoE) approach is recommended to address uncertainty. The Bioaccumulation Assessment Tool (BAT) guides a user through the process of collecting and generating various LoE required for assessing the bioaccumulation of neutral and ionizable organic chemicals in aquatic (water-respiring) and air-breathing organisms. The BAT includes data evaluation templates (DETs) to critically evaluate the reliability of the LoE used in the assessment. The DETs were developed from standardized testing guidance. The approach used in the BAT is consistent with OECD and SETAC WoE principles and facilitates the implementation of chemical policy objectives in chemical assessment and management. The recommended methods are also iterative and tiered, providing pragmatic methods to reduce unnecessary animal testing. General concepts of the BAT are presented and case study applications of the tool for hexachlorobenzene (HCB) and β-hexachlorocyclohexane (β-HCH) are demonstrated. The BAT provides a consistent and transparent WoE framework to address uncertainty in bioaccumulation assessment and is envisaged to evolve with scientific and regulatory developments. Integr Environ Assess Manag 2023;19:1235-1253. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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

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

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

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

Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions between Elexacaftor/Tezacaftor/Ivacaftor and Tacrolimus in Lung Transplant Recipients

Elexacaftor/tezacaftor/ivacaftor (ETI) treatment has potential benefits in lung transplant recipients, including improvements in extrapulmonary manifestations, such as gastrointestinal and sinus disease; however, ivacaftor is an inhibitor of cytochrome P450 3A (CYP3A) and may, therefore, pose a risk for elevated systemic exposure to tacrolimus. The aim of this investigation is to determine the impact of ETI on tacrolimus exposure and devise an appropriate dosing regimen to manage the risk of this drug-drug interaction (DDI). The CYP3A-mediated DDI of ivacaftor-tacrolimus was evaluated using a physiologically based pharmacokinetic (PBPK) modeling approach, incorporating CYP3A4 inhibition parameters of ivacaftor and in vitro enzyme kinetic parameters of tacrolimus. To further support the findings in PBPK modeling, we present a case series of lung transplant patients who received both ETI and tacrolimus. We predicted a 2.36-fold increase in tacrolimus exposure when co-administered with ivacaftor, which would require a 50% dose reduction of tacrolimus upon initiation of ETI treatment to avoid the risk of elevated systemic exposure. Clinical cases (N = 13) indicate a median 32% (IQR: -14.30, 63.80) increase in the dose-normalized tacrolimus trough level (trough concentration/weight-normalized daily dose) after starting ETI. These results indicate that the concomitant administration of tacrolimus and ETI may lead to a clinically significant DDI, requiring the dose adjustment of tacrolimus.

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

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

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

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

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.

Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty

Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.

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

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

Applications of In Silico Models to Predict Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of the withdrawal of pre-marketed drugs, typically attributed to oxidative stress, mitochondrial damage, disrupted bile acid homeostasis, and innate immune-related inflammation. DILI can be divided into intrinsic and idiosyncratic DILI with cholestatic liver injury as an important manifestation. The diagnosis of DILI remains a challenge today and relies on clinical judgment and knowledge of the insulting agent. Early prediction of hepatotoxicity is an important but still unfulfilled component of drug development. In response, in silico modeling has shown good potential to fill the missing puzzle. Computer algorithms, with machine learning and artificial intelligence as a representative, can be established to initiate a reaction on the given condition to predict DILI. DILIsym is a mechanistic approach that integrates physiologically based pharmacokinetic modeling with the mechanisms of hepatoxicity and has gained increasing popularity for DILI prediction. This article reviews existing in silico approaches utilized to predict DILI risks in clinical medication and provides an overview of the underlying principles and related practical applications.

Predictive performance and verification of physiologically based pharmacokinetic model of propylthiouracil

Background: Propylthiouracil (PTU) treats hyperthyroidism and thyroid crisis in all age groups. A variety of serious adverse effects can occur during clinical use and require attention to its pharmacokinetic and pharmacodynamic characteristics in various populations.

Objective: To provide information for individualized dosing and clinical evaluation of PTU in the clinical setting by developing a physiologically based pharmacokinetic (PBPK) model, predicting ADME characteristics, and extrapolating to elderly and pediatric populations.

Methods: Relevant databases and literature were retrieved to collect PTU’s pharmacochemical properties and ADME parameters, etc. A PBPK model for adults was developed using PK-Sim® software to predict tissue distribution and extrapolated to elderly and pediatric populations. The mean fold error (MFE) method was used to compare the differences between predicted and observed values to assess the accuracy of the PBPK model. The model was validated using PTU pharmacokinetic data in healthy adult populations.

Result: The MFE ratios of predicted to observed values of AUC0-t, Cmax, and Tmax were mainly within 0.5 and 2. PTU concentrations in various tissues are lower than venous plasma concentrations. Compared to healthy adults, the pediatric population requires quantitative adjustment to the appropriate dose to achieve the same plasma exposure levels, while the elderly do not require dose adjustments.

Conclusion: The PBPK model of PTU was successfully developed, externally validated, and applied to tissue distribution prediction and special population extrapolation, which provides a reference for clinical individualized drug administration and evaluation.

Metabolic, genetic, and pharmacokinetic parameters for the prediction of olanzapine efficacy

Clinical use of the a olanzapine has significantly different individual-to-individual outcomes. Accordingly, this study aimed to develop a means of predicting response to olanzapine using a combined approach based on pharmacokinetics, pharmacometabonomics, and genetic polymorphism. The olanzapine pharmacokinetics of 19 healthy volunteers treated with orally disintegrating tablets were determined using high-performance liquid chromatography-tandem mass spectrometry. Metabolic profiling and phenotyping were performed on the blood samples that remained after pharmacokinetic analysis using ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry. Uridine diphosphate-glucuronosyltransferase (UGT), tyrosine hydroxylase (TH), γ-aminobutyric acid transaminase (GABA-T), and succinic semialdehyde dehydrogenase (SSADH) were identified as key genes. The single nucleotide polymorphism genotypes most related to drug metabolism were investigated by polymerase chain reaction and Sanger sequencing. Forty-one metabolites (p < 0.05) are increased or decreased after treatment with olanzapine. Tryptophan metabolism, norepinephrine metabolism, and γ-aminobutyric acid metabolism were identified as being related to the effects of olanzapine. Subjects carrying rs1641031 AC and CC exhibited a 59.2% increase in the mean peak concentration (C_max) value and a 25.33% decrease in the mean oral clearance rate (CL/F) value, compared to that in subjects with the GABA-T rs1641031 AA genotype (p < 0.05). Moreover, polymorphism of the GABA-T gene has an impact on the metabolism of 5-hydroxytryptamine. Lysophosphatidylethanolamine (0:0/18:3), lysophosphatidylethanolamine (0:0/22:5), and octadecatrienoic acid distinguish subjects with high and low olanzapine drug oral clearance and are thus identified as biomarkers for predicting its efficacy.

Does the choice of applied physiologically‐based pharmacokinetics platform matter? A case study on simvastatin disposition and drug–drug interaction

Physiologically‐based pharmacokinetic (PBPK) models have an important role in drug discovery/development and decision making in regulatory submissions. This is facilitated by predefined PBPK platforms with user‐friendly graphical interface, such as Simcyp and PK‐Sim. However, evaluations of platform differences and the potential implications for disposition‐related applications are still lacking. The aim of this study was to assess how PBPK model development, input parameters, and model output are affected by the selection of PBPK platform. This is exemplified via the establishment of simvastatin PBPK models (workflow, final models, and output) in PK‐Sim and Simcyp as representatives of established whole‐body PBPK platforms. The major finding was that the choice of PBPK platform influenced the model development strategy and the final model input parameters, however, the predictive performance of the simvastatin models was still comparable between the platforms. The main differences between the structure and implementation of Simcyp and PK‐Sim were found in the absorption and distribution models. Both platforms predicted equally well the observed simvastatin (lactone and acid) pharmacokinetics (20–80 mg), BCRP and OATP1B1 drug–gene interactions (DGIs), and drug–drug interactions (DDIs) when co‐administered with CYP3A4 and OATP1B1 inhibitors/inducers. This study illustrates that in‐depth knowledge of established PBPK platforms is needed to enable an assessment of the consequences of PBPK platform selection. Specifically, this work provides insights on software differences and potential implications when bridging PBPK knowledge between Simcyp and PK‐Sim users. Finally, it provides a simvastatin model implemented in both platforms for risk assessment of metabolism‐ and transporter‐mediated DGIs and DDIs.

Physiologically based modelling of the antiplatelet effect of aspirin: A tool to characterize drug responsiveness and inform precision dosing

A computational approach involving mathematical modeling and in silico experiments was used to characterize the determinants of extent and duration of platelet cyclooxygenase (COX)-1 inhibition by aspirin and design precision dosing in patients with accelerated platelet turnover or reduced drug bioavailability. To this purpose, a recently developed physiologically-based pharmacokinetics (PK) and pharmacodynamics (PD) model of low-dose aspirin in regenerating platelets and megakaryocytes, was used to predict the main features and determinants of platelet COX-1 inhibition. The response to different aspirin regimens in healthy subjects and in pathological conditions associated with alterations in aspirin PK (i.e., severely obese subjects) or PD (i.e., essential thrombocytemya patients), were simulated. A model sensitivity analysis was performed to identify the main processes influencing COX-1 dynamics. In silico experiments and sensitivity analyses indicated a major role for megakaryocytes and platelet turnover in determining the extent and duration of COX-1 inhibition by once-daily, low-dose aspirin. They also showed the superiority of reducing the dosing interval vs increasing the once-daily dose in conditions of increased platelet turnover, while suggested specific dose adjustments in conditions of possible reduction in drug bioavailability. In conclusion, the consistency of our model-based findings with experimental data from studies in healthy subjects and patients with essential thrombocythemia supports the potential of our approach for describing the determinants of platelet inhibition by aspirin and informing precision dosing which may guide personalized antithrombotic therapy in different patient populations, especially in those under-represented in clinical trials or in those associated with poor feasibility.

MATE1 Deficiency Exacerbates Dofetilide-Induced Proarrhythmia

Dofetilide is a rapid delayed rectifier potassium current inhibitor widely used to prevent the recurrence of atrial fibrillation and flutter. The clinical use of this drug is associated with increases in QTc interval, which predispose patients to ventricular cardiac arrhythmias. The mechanisms involved in the disposition of dofetilide, including its movement in and out of cardiomyocytes, remain unknown. Using a xenobiotic transporter screen, we identified MATE1 (SLC47A1) as a transporter of dofetilide and found that genetic knockout or pharmacological inhibition of MATE1 in mice was associated with enhanced retention of dofetilide in cardiomyocytes and increased QTc prolongation. The urinary excretion of dofetilide was also dependent on the MATE1 genotype, and we found that this transport mechanism provides a mechanistic basis for previously recorded drug-drug interactions of dofetilide with various contraindicated drugs, including bictegravir, cimetidine, ketoconazole, and verapamil. The translational significance of these observations was examined with a physiologically-based pharmacokinetic model that adequately predicted the drug-drug interaction liabilities in humans. These findings support the thesis that MATE1 serves a conserved cardioprotective role by restricting excessive cellular accumulation and warrant caution against the concurrent administration of potent MATE1 inhibitors and cardiotoxic substrates with a narrow therapeutic window.

Developing New Treatments for COVID-19 through Dual-Action Antiviral/Anti-Inflammatory Small Molecules and Physiologically Based Pharmacokinetic Modeling

Broad-spectrum antiviral agents that are effective against many viruses are difficult to develop, as the key molecules, as well as the biochemical pathways by which they cause infection, differ largely from one virus to another. This was more strongly highlighted by the COVID-19 pandemic, which found health systems all over the world largely unprepared and proved that the existing armamentarium of antiviral agents is not sufficient to address viral threats with pandemic potential. The clinical protocols for the treatment of COVID-19 are currently based on the use of inhibitors of the inflammatory cascade (dexamethasone, baricitinib), or inhibitors of the cytopathic effect of the virus (monoclonal antibodies, molnupiravir or nirmatrelvir/ritonavir), using different agents. There is a critical need for an expanded armamentarium of orally bioavailable small-molecular medicinal agents, including those that possess dual antiviral and anti-inflammatory (AAI) activity that would be readily available for the early treatment of mild to moderate COVID-19 in high-risk patients. A multidisciplinary approach that involves the use of in silico screening tools to identify potential drug targets of an emerging pathogen, as well as in vitro and in vivo models for the determination of a candidate drug’s efficacy and safety, are necessary for the rapid and successful development of antiviral agents with potentially dual AAI activity. Characterization of candidate AAI molecules with physiologically based pharmacokinetics (PBPK) modeling would provide critical data for the accurate dosing of new therapeutic agents against COVID-19. This review analyzes the dual mechanisms of AAI agents with potential anti-SARS-CoV-2 activity and discusses the principles of PBPK modeling as a conceptual guide to develop new pharmacological modalities for the treatment of COVID-19.

Development of a Translational Exposure-Bracketing Approach to Streamline the Development of Hormonal Contraceptive Drug Products

Worldwide, 922 million women of reproductive age (or their partners) use some sort of contraception to prevent pregnancy. Oral combined hormonal contraceptives (CHC) typically utilize a combination of a progestin and an estrogen. CHC are potentially at risk to metabolic drug-drug interaction (DDI) via CYP3A4, the main enzyme involved in the oxidative metabolism of ethinyl estradiol (EE) and most progestins, e.g. levonorgestrel (LNG) and drospirenone (DRSP). Recently, the FDA issued a guidance addressing metabolic DDIs in the realm of CHC, establishing an overall class-based recommendation with respect to avoidance of CYP3A4 induction interactions. Given that different progestins have varying magnitudes of fraction metabolized by CYP3A4 (fm_CYP3A4 ), it would be of clinical benefit to determine if all progestins are at the same risk to CYP3A4-mediated metabolic DDI. LNG and DRSP are commonly used progestins that are at the margins of the rifampicin induction effect observed in vivo since they have the relatively lowest and highest fm_CYP3A4 among commonly used CHC formulations containing norgestimate, desogestrel, norgestrel, and norethindrone. Therefore, we applied a multi-pronged strategy, i.e. (1) development of the PBPK models, (2) comparison of the effect of CYP3A inducers and inhibitors on DRSP vs. LNG, and (3) providing the clinical-practice context based on real world data, to explore the difference in DDI risk for oral CHCs.

Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development

Drug metabolism and pharmacokinetics (DMPK) is an important branch of pharmaceutical sciences. The nature of ADME (absorption, distribution, metabolism, excretion) and PK (pharmacokinetics) inquiries during drug discovery and development has evolved in recent years from being largely descriptive to seeking a more quantitative and mechanistic understanding of the fate of drug candidates in biological systems. Tremendous progress has been made in the past decade, not only in the characterization of physiochemical properties of drugs that influence their ADME, target organ exposure, and toxicity, but also in the identification of design principles that can minimize drug-drug interaction (DDI) potentials and reduce the attritions. The importance of membrane transporters in drug disposition, efficacy, and safety, as well as the interplay with metabolic processes, has been increasingly recognized. Dramatic increases in investments on new modalities beyond traditional small and large molecule drugs, such as peptides, oligonucleotides, and antibody-drug conjugates, necessitated further innovations in bioanalytical and experimental tools for the characterization of their ADME properties. In this review, we highlight some of the most notable advances in the last decade, and provide future perspectives on potential major breakthroughs and innovations in the translation of DMPK science in various stages of drug discovery and development.

Microfluidic Organ-on-a-Chip System for Disease Modeling and Drug Development

An organ-on-a-chip is a device that combines micro-manufacturing and tissue engineering to replicate the critical physiological environment and functions of the human organs. Therefore, it can be used to predict drug responses and environmental effects on organs. Microfluidic technology can control micro-scale reagents with high precision. Hence, microfluidics have been widely applied in organ-on-chip systems to mimic specific organ or multiple organs in vivo. These models integrated with various sensors show great potential in simulating the human environment. In this review, we mainly introduce the typical structures and recent research achievements of several organ-on-a-chip platforms. We also discuss innovations in models applied to the fields of pharmacokinetics/pharmacodynamics, nano-medicine, continuous dynamic monitoring in disease modeling, and their further applications in other fields.

Application of Physiologically Based Pharmacokinetic Modeling in Preclinical Studies: A Feasible Strategy to Practice the Principles of 3Rs

Pharmacokinetic characterization plays a vital role in drug discovery and development. Although involving numerous laboratory animals with error-prone, labor-intensive, and time-consuming procedures, pharmacokinetic profiling is still irreplaceable in preclinical studies. With physiologically based pharmacokinetic (PBPK) modeling, the in vivo profiles of drug absorption, distribution, metabolism, and excretion can be predicted. To evaluate the application of such an approach in preclinical investigations, the plasma pharmacokinetic profiles of seven commonly used probe substrates of microsomal enzymes, including phenacetin, tolbutamide, omeprazole, metoprolol, chlorzoxazone, nifedipine, and baicalein, were predicted in rats using bottom-up PBPK models built with in vitro data alone. The prediction's reliability was assessed by comparison with in vivo pharmacokinetic data reported in the literature. The overall predicted accuracy of PBPK models was good with most fold errors within 2, and the coefficient of determination (R2) between the predicted concentration data and the observed ones was more than 0.8. Moreover, most of the observation dots were within the prediction span of the sensitivity analysis. We conclude that PBPK modeling with acceptable accuracy may be incorporated into preclinical studies to refine in vivo investigations, and PBPK modeling is a feasible strategy to practice the principles of 3Rs.

PBPK Modeling on Organs-on-Chips: An Overview of Recent Advancements

Organ-on-a-chip (OoC) is a new and promising technology, which aims to improve the efficiency of drug development and realize personalized medicine by simulating in vivo environment in vitro. Physiologically based pharmacokinetic (PBPK) modeling is believed to have the advantage of better reflecting the absorption, distribution, metabolism and excretion process of drugs in vivo than traditional compartmental or non-compartmental pharmacokinetic models. The combination of PBPK modeling and organ-on-a-chip is believed to provide a strong new tool for new drug development and have the potential to replace animal testing. This article provides the recent development of organ-on-a-chip technology and PBPK modeling including model construction, parameter estimation and validation strategies. Application of PBPK modeling on Organ-on-a-Chip (OoC) has been emphasized, and considerable progress has been made. PBPK modeling on OoC would become an essential part of new drug development, personalized medicine and other fields.

What Is a 2021 Reference Body?

The historical 1975 Reference Man is a ‘model’ that had been used as a basis for the calculation of radiation doses, metabolism, pharmacokinetics, sizes for organ transplantation and ergonomic optimizations in the industry, e.g., to plan dimensions of seats and other formats. The 1975 Reference Man was not an average individual of a population; it was based on the multiple characteristics of body compositions that at that time were available, i.e., mainly from autopsy data. Faced with recent technological advances, new mathematical models and socio-demographic changes within populations characterized by an increase in elderly and overweight subjects a timely ‘state-of-the-art’ 2021 Reference Body are needed. To perform this, in vivo human body composition data bases in Kiel, Baton Rouge, San Francisco and Honolulu were analyzed and detailed 2021 Reference Bodies, and they were built for both sexes and two age groups (≤40 yrs and >40 yrs) at BMIs of 20, 25, 30 and 40 kg/m2. We have taken an integrative approach to address ‘structure−structure’ and ‘structure−function’ relationships at the whole-body level using in depth body composition analyses as assessed by gold standard methods, i.e., whole body Magnetic Resonance Imaging (MRI) and the 4-compartment (4C-) model (based on deuterium dilution, dual-energy X-ray absorptiometry and body densitometry). In addition, data obtained by a three-dimensional optical scanner were used to assess body shape. The future applications of the 2021 Reference Body relate to mathematical modeling to address complex metabolic processes and pharmacokinetics using a multi-level/multi-scale approach defining health within the contexts of neurohumoral and metabolic control.

A PBPK Model of Ternary Cyclodextrin Complex of ST-246 Was Built to Achieve a Reasonable IV Infusion Regimen for the Treatment of Human Severe Smallpox

ST-246 is an oral drug against pathogenic orthopoxvirus infections. An intravenous formulation is required for some critical patients. A ternary complex of ST-246/meglumine/hydroxypropyl-β-cyclodextrin with well-improved solubility was successfully developed in our institute. The aim of this study was to achieve a reasonable intravenous infusion regimen of this novel formulation by a robust PBPK model based on preclinical pharmacokinetic studies. The pharmacokinetics of ST-246 after intravenous injection at different doses in rats, dogs, and monkeys were conducted to obtain clearances. The clearance of humans was generated by using the allometric scaling approach. Tissue distribution of ST-246 was conducted in rats to obtain tissue partition coefficients (K_p). The PBPK model of the rat was first built using in vivo clearance and K_p combined with in vitro physicochemical properties, unbound fraction, and cyclodextrin effect parameters of ST-246. Then the PBPK model was transferred to a dog and monkey and validated simultaneously. Finally, pharmacokinetic profiles after IV infusion at different dosages utilizing the human PBPK model were compared to the observed oral PK profile of ST-246 at therapeutic dosage (600 mg). The mechanistic PBPK model described the animal PK behaviors of ST-246 via intravenous injection and infusion with fold errors within 1.2. It appeared that 6h-IV infusion at 5 mg/kg BID produced similar C_max and AUC as oral administration at 600 mg. A PBPK model of ST-246 was built to achieve a reasonable regimen of IV infusion for the treatment of severe smallpox, which will facilitate the clinical translation of this novel formulation.

Exploration and application of a liver-on-a-chip device in combination with modelling and simulation for quantitative drug metabolism studies

Microphysiological systems (MPS) are complex and more physiologically realistic cellular in vitro tools that aim to provide more relevant human in vitro data for quantitative prediction of clinical pharmacokinetics while also reducing the need for animal testing. The PhysioMimix liver-on-a-chip integrates medium flow with hepatocyte culture and has the potential to be adopted for in vitro studies investigating the hepatic disposition characteristics of drug candidates. The current study focusses on liver-on-a-chip system exploration for multiple drug metabolism applications. Characterization of cytochrome P450 (CYP), UDP-glucuronosyl transferase (UGT) and aldehyde oxidase (AO) activities was performed using 15 drugs and in vitro to in vivo extrapolation (IVIVE) was assessed for 12 of them. Next, the utility of the liver-on-a-chip for estimation of the fraction metabolized (f_m) via specific biotransformation pathways of quinidine and diclofenac was established. Finally, the metabolite identification opportunities were also explored using efavirenz as an example drug with complex primary and secondary metabolism involving a combination of CYP, UGT and sulfotransferase enzymes. A key aspect of these investigations was the application of mathematical modelling for improved parameter calculation. Such approaches will be required for quantitative assessment of metabolism and/or transporter processes in systems where medium flow and system compartments result in non-homogeneous drug concentrations. In particular, modelling was used to explore the effect of evaporation from the medium and it was found that the intrinsic clearance (CL_int) might be underestimated by up to 40% for low clearance compounds if evaporation is not accounted for. Modelling of liver-on-a-chip in vitro data also enhanced the approach to f_m estimation allowing objective assessment of metabolism models of different complexity. The resultant diclofenac f_m,UGT of 0.64 was highly comparable with values reported previously in the literature. The current study demonstrates the integration of mathematical modelling with experimental liver-on-a-chip studies and illustrates how this approach supports generation of high quality of data from complex in vitro cellular systems.

Development of Physiologically Based Pharmacokinetic Model and Assessment of the Impact of Renal Underdevelopment in Preterm Infants on the Pharmacokinetics of Aminophylline

Objective:

To develop a physiologically based pharmacokinetic (PBPK) model for individualization of the dosing regimen considering the physiological requirements of these preterm neonates.

Methods:

The study comprised preterm newborns with fewer than 34 weeks of gestation and six apneic episodes in 24 h. A PBPK model was created using PK-SIM (version 9, update 1, GitHub, San Francisco, CA, USA). A PBPK model is built using a typical loading dosage of 5 mg/kg and a maintenance dose of 1.5 mg/kg. Based on the verified base model, a PBPK model representing renal underdevelopment based on nRIFLE/pRIFLE categorization was developed.

Results:

The PK parameters of Aminophylline were computed using the PBPK model. As per the model prediction, T1/2 and area under the curve reduced as postnatal age increased, and in the event of renal underdevelopment, even while C max for patients under R (RISK), I (injury) was within the therapeutic range; it was greater compared to preterm without any renal complications. Mean C max (mol/L) was 59.53 and for R, I, and F (FAILURE) categories the values were 83.04, 99.69, and 126.98, respectively.

Conclusion:

The model was created using appropriate drug, study subject, and dosage protocol inputs. The established PBPK model could help in individualizing aminophylline dose in preterm babies.

Physiologically Based Pharmacokinetic Modelling and Simulation to Predict the Plasma Concentration Profile of Doxorubicin

Doxorubicin (DOX) is still an important anticancer agent despite its tricky pharmacokinetics (PK) and toxicity potential. The advent of systems pharmacology enables the construction of PK models able to predict the concentration profiles of drugs and shed light on the underlying mechanisms involved in PK and pharmacodynamics (PD). By utilizing existing published data and by analysing two clinical case studies we attempt to create physiologically based pharmacokinetic (PBPK) models for DOX using widely accepted methodologies. Based on two different approaches on three different key points we derived eight plausible models. The validation of the models provides evidence that is all performing as designed and opens the way for further exploitation by integrating metabolites and pharmacogenomic information.

Assessment of the predictive capability of modelling and simulation to determine bioequivalence of inhaled drugs: A systematic review

OBJECTIVES

There are a multitude of different modelling techniques that have been used for inhaled drugs. The main objective of this review was to conduct an exhaustive survey of published mathematical models in the area of asthma and chronic obstructive pulmonary disease (COPD) for inhalation drugs. Additionally, this review will attempt to assess the applicability of these models to assess bioequivalence (BE) of orally inhaled products (OIPs).

EVIDENCE ACQUISITION

PubMed, Science Direct, Web of Science, and Scopus databases were searched from 1996 to 2020, to find studies that described mathematical models used for inhaled drugs in asthma/COPD.

RESULTS

50 articles were finally included in this systematic review. This research identified 22 articles on in silico aerosol deposition models, 20 articles related to population pharmacokinetics and 8 articles on physiologically based pharmacokinetic modelling (PBPK) modelling for inhaled drugs in asthma/COPD. Among all the aerosol deposition models, computational fluid dynamics (CFD) simulations are more likely to predict regional aerosol deposition pattern in human respiratory tracts. Across the population PK articles, body weight, gender, age and smoking status were the most common covariates that were found to be significant. Further, limited published PBPK models reported approximately 29 parameters relevant for absorption and distribution of inhaled drugs. The strengths and weaknesses of each modelling technique has also been reviewed.

CONCLUSION

Overall, while there are different modelling techniques that have been used for inhaled drugs in asthma and COPD, there is very limited application of these models for assessment of bioequivalence of OIPs. This review also provides a ready reference of various parameters that have been considered in various models which will aid in evaluation if one model or hybrid in silico models need to be considered when assessing bioequivalence of OIPs.

Bioinformatics and Computer Simulation approaches to the discovery and analysis of Bioactive Peptides

The traditional process of separating and purifying bioactive peptides is laborious and time-consuming. Using a traditional process to identify is difficult, and there is a lack of fast and accurate activity evaluation methods. How to extract bioactive peptides quickly and efficiently is still the focus of bioactive peptides research. In order to improve the present situation of the research, bioinformatics techniques and peptidome methods are widely used in this field. At the same time, bioactive peptides have their own specific pharmacokinetic characteristics, so computer simulation methods have incomparable advantages in studying the pharmacokinetics and pharmacokinetic-pharmacodynamic correlation models of bioactive peptides. The purpose of this review is to summarize the combined applications of bioinformatics and computer simulation methods in the study of bioactive peptides, with focuses on the role of bioinformatics in simulating the selection of enzymatic hydrolysis and precursor proteins, activity prediction, molecular docking, physicochemical properties, and molecular dynamics. Our review shows that new bioactive peptide molecular sequences with high activity can be obtained by computer-aided design. The significance of the pharmacokinetic-pharmacodynamic correlation model in the study of bioactive peptides is emphasized. Finally, some problems and future development potential of bioactive peptides binding new technologies are prospected.

PBPK Modeling to Simulate the Fate of Compounds in Living Organisms

Pharmacokinetics study the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.

Best practices in current models mimicking drug permeability in the gastrointestinal tract - an UNGAP review

The absorption of orally administered drug products is a complex, dynamic process, dependent on a range of biopharmaceutical properties; notably the aqueous solubility of a molecule, stability within the gastrointestinal tract (GIT) and permeability. From a regulatory perspective, the concept of high intestinal permeability is intrinsically linked to the fraction of the oral dose absorbed. The relationship between permeability and the extent of absorption means that experimental models of permeability have regularly been used as a surrogate measure to estimate the fraction absorbed. Accurate assessment of a molecule's intestinal permeability is of critical importance during the pharmaceutical development process of oral drug products, and the current review provides a critique of in vivo, in vitro and ex vivo approaches. The usefulness of in silico models to predict drug permeability is also discussed and an overview of solvent systems used in permeability assessments is provided. Studies of drug absorption in humans are an indirect indicator of intestinal permeability, but in vitro and ex vivo tools provide initial screening approaches are important tools for direct assessment of permeability in drug development. Continued refinement of the accuracy of in silico approaches and their validation with human in vivo data will facilitate more efficient characterisation of permeability earlier in the drug development process and will provide useful inputs for integrated, end-to-end absorption modelling.