Mechanism-based prediction of particle size-dependent dissolution and absorption: Cilostazol pharmacokinetics in dogs

Formulation Strategy of BCS-II Drugs by Coupling Mechanistic In-Vitro and Nonclinical In-Vivo Data with PBPK: Fundamentals of Absorption-Dissolution to Parameterization of Modelling and Simulation

BCS class II candidates pose challenges in drug development due to their low solubility and permeability. Researchers have explored various techniques; co-amorphous and solid dispersion are major approaches to enhance in-vitro drug solubility and dissolution. However, in-vivo oral bioavailability remains challenging. Physiologically based pharmacokinetic (PBPK) modeling with a detailed understanding of drug absorption, distribution, metabolism, and excretion (ADME) using a mechanistic approach is emerging. This review summarizes the fundamentals of the PBPK, dissolution-absorption models, parameterization of oral absorption for BCS class II drugs, and provides information about newly emerging artificial intelligence/machine learning (AI/ML) linked PBPK approaches with their advantages, disadvantages, challenges and areas of further exploration. Additionally, the fully integrated workflow for formulation design for investigational new drugs (INDs) and virtual bioequivalence for generic molecules falling under BCS-II are discussed.

Pharmacokinetic Prediction of Immediate- and Extended-Release Tablets for Patients with Liver Disease Using Whole Body Physiologically-Based Pharmacokinetic Modeling for the Antipsychotic Drug Quetiapine

Although quetiapine metabolism occurs extensively in the liver and careful dosing is recommended in patients with liver disease, there has been a paucity of pharmacometric studies to adjust the clinical dose of quetiapine according to liver-disease severity. This study aimed to establish a whole-body, physiologically-based pharmacokinetic (WB-PBPK) model to explain interindividual variability in quetiapine PK and quantitatively predict PK in patients with liver disease. The developed WB-PBPK model well described the PK characteristics of different quetiapine regimens in healthy populations. The PK predictions could also be applied to patients with schizophrenia (without significant differences from healthy subjects). For the same total dose of quetiapine, both immediate-release (IR) and extended-release (ER) tablets showed significantly increased exposure and decreased clearance in patients with liver disease compared to healthy subjects. The model showed that steady-state plasma quetiapine concentrations exceeded the usual therapeutic range after multiple doses of IR tablets 250 mg three times daily or ER tablets 800 mg once daily in patients with liver disease. Therefore, the doses of quetiapine IR or ER tablets could be reduced by 0.10-0.50 times depending on liver-disease severity, so that mean steady-state plasma concentrations could be positioned near the therapeutic range. WB-PBPK modeling for quetiapine enabled quantitative prediction of PK according to IR or ER formulation and liver-disease severity. The results of this study provide useful data for improving the therapeutic use of quetiapine by enabling dose selection based on formulation and liver-disease severity.

Personalized Dosing of Medicines for Children: A Primer on Pediatric Pharmacometrics for Clinicians

The widespread use of drugs for unapproved purposes remains common in children, primarily attributable to practical, ethical, and financial constraints associated with pediatric drug research. Pharmacometrics, the scientific discipline that involves the application of mathematical models to understand and quantify drug effects, holds promise in advancing pediatric pharmacotherapy by expediting drug development, extending applications, and personalizing dosing. In this review, we delineate the principles of pharmacometrics, and explore its clinical applications and prospects. The fundamental aspect of any pharmacometric analysis lies in the selection of appropriate methods for quantifying pharmacokinetics and pharmacodynamics. Population pharmacokinetic modeling is a data-driven method ('top-down' approach) to approximate population-level pharmacokinetic parameters, while identifying factors contributing to inter-individual variability. Model-informed precision dosing is increasingly used to leverage population pharmacokinetic models and patient data, to formulate individualized dosing recommendations. Physiologically based pharmacokinetic models integrate physicochemical drug properties with biological parameters ('bottom-up approach'), and is particularly valuable in situations with limited clinical data, such as early drug development, assessing drug-drug interactions, or adapting dosing for patients with specific comorbidities. The effective implementation of these complex models hinges on strong collaboration between clinicians and pharmacometricians, given the pivotal role of data availability. Promising advancements aimed at improving data availability encompass innovative techniques such as opportunistic sampling, minimally invasive sampling approaches, microdialysis, and in vitro investigations. Additionally, ongoing research efforts to enhance measurement instruments for evaluating pharmacodynamics responses, including biomarkers and clinical scoring systems, are expected to significantly bolster our capacity to understand drug effects in children.

A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

Proof of concept of physiologically based pharmacokinetic modelling in paediatric acute lymphoblastic leukaemia

Physiologically based pharmacokinetic (PBPK) modelling is an alternative modelling technique that is increasingly used in pharmacokinetics. Due to its nature, it can be complementarily employed to population pharmacokinetics, especially when it comes to small population size. Here, we report the proof of concept of its application to accurately describe the pharmacokinetics of a recombinant L-asparaginase in paediatric patients with acute lymphoblastic leukaemia. Data from two randomized, double-blind, phase II/III clinical studies (MC-ASP.4/ALL; MC-ASP.5/ALL) were included to setup and evaluate the final model, respectively. Final population values for basic pharmacokinetic parameters were calculated (clearance: 0.0569 L/h/19.5 kg, volume of distribution: 1.251 L, half-life: 18.5 h, trough concentration: 140.9 IU/L). Pharmacokinetic parameter prediction as well as predictive performance of the model proofed to be comparable to a separately developed population pharmacokinetic model with 13% deviation in predicted median L-asparaginase trough levels. To the best of our knowledge, this is the first whole-body PBPK model of a non-antibody therapeutic protein.

Physiologically based pharmacokinetic modeling of ritonavir-oxycodone drug interactions and its implication for dosing strategy

The concomitant administration of ritonavir and oxycodone may significantly increase the plasma concentrations of oxycodone. This study was aimed to simulate DDI between ritonavir and oxycodone, a widely used opioid, and to formulate dosing protocols for oxycodone by using physiologically based pharmacokinetic (PBPK) modeling. We developed a ritonavir PBPK model incorporating induction and competitive and time-dependent inhibition of CYP3A4 and competitive inhibition of CYP2D6. The ritonavir model was evaluated with observed clinical pharmacokinetic data and validated for its CYP3A4 inhibition potency. We then used the model to simulate drug interactions between oxycodone and ritonavir under various dosing scenarios. The developed model captured the pharmacokinetic characteristics of ritonavir from clinical studies. The model also accurately predicts exposure changes of midazolam, triazolam, and oxycodone in the presence of ritonavir. According to model simulations, the steady-state maximum, minimum and average concentrations of oxycodone increased by up to 166% after co-administration with ritonavir, and the total exposure increased by approximately 120%. To achieve similar steady-state concentrations, halving the dose with an unchanged dosing interval or doubling the dosing interval with an unaltered single dose should be practical for oxycodone, whether formulated in uncoated or controlled-release tablets during long-term co-medication with ritonavir. The results revealed exposure-related risks of oxycodone-ritonavir interactions that have not been studied clinically and emphasized PBPK as a workable method to direct judicious dosage.

In Silico Modeling Approaches Coupled with In Vitro Characterization in Predicting In Vivo Performance of Drug Delivery System Formulations

Optimization of the in vivo performance of dosage forms in humans is essential in developing not only conventional formulations but also drug delivery system (DDS) formulations. Although animal experiments are still useful for these formulations, in silico approaches have become increasingly important for DDS formulations with regard to species-specific differences in physiology that can affect the in vivo performance of dosage forms between animals and humans. Furthermore, it is also important to couple in vitro characterizations with in silico models to predict in vivo performance in humans precisely. In this review article, I summarized in vitro-in silico approaches to predicting the in vivo performance of oral DDS formulations (amorphous solid dispersions, lipid-based formulations, nanosized formulations, cyclodextrins-based formulations, sustained release products, enteric coat products, and orally disintegrating tablets) and parenteral DDS formulations (cyclodextrins-based formulations, liposomes, and inhaled formulations).

Evaluating the Pharmacokinetics of Fentanyl in the Brain Extracellular Fluid, Saliva, Urine, and Plasma of Newborns from Transplacental Exposure from Parturient Mothers Dosed with Epidural Fentanyl Utilizing PBPK Modeling

BACKGROUND AND OBJECTIVE

Fentanyl can mitigate the mother and newborn complications resulting from labor pain. However, fentanyl shows a narrow therapeutic index between its respiratory depressive and analgesic effects. Thus, prenatally acquired high fentanyl levels in the newborn brain extracellular fluid (bECF) may induce respiratory depression which requires therapeutic drug monitoring (TDM). TDM using saliva and urine in newborns can reduce the possibility of infections and distress associated with TDM using blood. The objective of this study was to develop  a physiologically based pharmacokinetic (PBPK) model to predict fentanyl concentrations in different newborn tissues due to intrauterine exposure.

METHODS

A fentanyl PBPK model in adults after intravenous and epidural administration was built, validated, and scaled to pregnancy and newborn populations. The dose that the newborn received transplacentally at birth was calculated using the pregnancy model. Then, the newborn bECF, saliva, plasma, and urine concentrations after such a dose were predicted using the newborn PBPK model.

RESULTS

After a maternal epidural dose of fentanyl 245 µg, the predicted newborn plasma and bECF levels were below the toxicity thresholds. Furthermore, the salivary threshold levels in newborns for fentanyl analgesic and respiratory depression effects were estimated to be 0.39 and 14.7-18.2 ng/ml, respectively.

CONCLUSION

The salivary TDM of fentanyl in newborns can be useful in newborns exposed to intrauterine exposure from parturient females dosed with epidural fentanyl. However, newborn-specific values of µ-opioid receptors IC50 for respiratory depression are needed.

Leveraging the use of in vitro and computational methods to support the development of enabling oral drug products: An InPharma commentary

Due to the strong tendency towards poorly soluble drugs in modern development pipelines, enabling drug formulations such as amorphous solid dispersions, cyclodextrins, co-crystals and lipid-based formulations are frequently applied to solubilize or generate supersaturation in gastrointestinal fluids, thus enhancing oral drug absorption. Although many innovative in vitro and in silico tools have been introduced in recent years to aid development of enabling formulations, significant knowledge gaps still exist with respect to how best to implement them. As a result, the development strategy for enabling formulations varies considerably within the industry and many elements of empiricism remain. The InPharma network aims to advance a mechanistic, animal-free approach to the assessment of drug developability. This commentary focuses current status and next steps that will be taken in InPharma to identify and fully utilize 'best practice' in vitro and in silico tools for use in physiologically based biopharmaceutic models.

The Analysis of Pethidine Pharmacokinetics in Newborn Saliva, Plasma, and Brain Extracellular Fluid After Prenatal Intrauterine Exposure from Pregnant Mothers Receiving Intramuscular Dose Using PBPK Modeling

BACKGROUND AND OBJECTIVE

Pethidine (meperidine) can decrease labor pain-associated mother's hyperventilation and high cortisol-induced newborn complications. However, prenatal transplacentally acquired pethidine can cause side effects in newborns. High pethidine concentrations in the newborn brain extracellular fluid (bECF) can cause a serotonin crisis. Therapeutic drug monitoring (TDM) in newborns' blood distresses them and increases infection incidence, which can be overcome by using salivary TDM. Physiologically based pharmacokinetic (PBPK) modeling can predict drug concentrations in newborn plasma, saliva, and bECF after intrauterine pethidine exposure.

METHODS

A healthy adult PBPK model was constructed, verified, and scaled to newborn and pregnant populations after intravenous and intramuscular pethidine administration. The pregnancy PBPK model was used to predict the newborn dose received transplacentally at birth, which was used as input to the newborn PBPK model to predict newborn plasma, saliva, and bECF pethidine concentrations and set correlation equations between them.

RESULTS

Pethidine can be classified as a Salivary Excretion Classification System class II drug. The developed PBPK model predicted that, after maternal pethidine intramuscular doses of 100 mg and 150 mg, the newborn plasma and bECF concentrations were below the toxicity thresholds. Moreover, it was estimated that newborn saliva concentrations of 4.7 µM, 11.4 µM, and 57.7 µM can be used as salivary threshold concentrations for pethidine analgesic effects, side effects, and the risk for serotonin crisis, respectively, in newborns.

CONCLUSION

It was shown that saliva can be used for pethidine TDM in newborns during the first few days after delivery to mothers receiving pethidine.

The investigation of the complex population-drug-drug interaction between ritonavir-boosted lopinavir and chloroquine or ivermectin using physiologically-based pharmacokinetic modeling

OBJECTIVES

Therapy failure caused by complex population-drug-drug (PDDI) interactions including CYP3A4 can be predicted using mechanistic physiologically-based pharmacokinetic (PBPK) modeling. A synergy between ritonavir-boosted lopinavir (LPVr), ivermectin, and chloroquine was suggested to improve COVID-19 treatment. This work aimed to study the PDDI of the two CYP3A4 substrates (ivermectin and chloroquine) with LPVr in mild-to-moderate COVID-19 adults, geriatrics, and pregnancy populations.

METHODS

The PDDI of LPVr with ivermectin or chloroquine was investigated. Pearson's correlations between plasma, saliva, and lung interstitial fluid (ISF) levels were evaluated. Target site (lung epithelial lining fluid [ELF]) levels of ivermectin and chloroquine were estimated.

RESULTS

Upon LPVr coadministration, while the chloroquine plasma levels were reduced by 30, 40, and 20%, the ivermectin plasma levels were increased by a minimum of 425, 234, and 453% in adults, geriatrics, and pregnancy populations, respectively. The established correlation equations can be useful in therapeutic drug monitoring (TDM) and dosing regimen optimization.

CONCLUSIONS

Neither chloroquine nor ivermectin reached therapeutic ELF levels in the presence of LPVr despite reaching toxic ivermectin plasma levels. PBPK modeling, guided with TDM in saliva, can be advantageous to evaluate the probability of reaching therapeutic ELF levels in the presence of PDDI, especially in home-treated patients.

Elucidating the Particle Size Effect of Andrographolide Suspensions on Their IVIVC Performance in Oral Absorption

The study aimed to explore the size effect on the in vitro-in vivo correlation (IVIVC) in the oral absorption of andrographolide nanosuspensions (Ag-NS). Ag-NS with controllable particle sizes were prepared by ultrasonic dispersion method, and the formulation and process parameters were optimized through single factor experiments using mean particle size, polydispersity index, and stability as evaluation indicators. The morphology of Ag-NS was observed by scanning electron microscopy (SEM), and the crystalline state of the nanosuspensions was characterized by X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC). The dissolution tests were carried out with the paddle method in two different mediums simulating the pH conditions in intestinal fluid pH 6.8 and gastric fluid (pH 1.2), respectively. The pharmacokinetic behaviors were investigated in rats after oral administration, and a deconvolution approach was introduced to determine the correlation between in vitro dissolution and in vivo absorption (IVIVC). The formulation with the use of lecithin and PEG-800 as stabilizers showed its potential in the size-controllable preparation of Ag-NS. Via altering the ultrasonication amplitude and time, three Ag-NS suspensions with particle sizes of particle size, i.e., Ag-NS 250 (244.3 ± 0.4 nm), Ag-NS 450 (464.3 ± 32.2 nm), Ag-NS 1000 (1015 ± 36.1 nm) were prepared. Their morphological and crystal characteristics did not change during the size reduction process, but both of their in vitro dissolution and in vivo absorption were improved. Relatively better IVIVC performance was observed with the in vitro dissolution data at pH 6.8 (r > 0.9). With the reduction of particle size, the in vivo absorption fraction was more closed to the level of the in vitro dissolution. In conclusion, the decrease in particle size would improve the dissolution and absorption of Ag-NS, and also affect their IVIVC performance. The study would facilitate the design and quality control of Ag-NS in terms of particle size and dissolution specifications.

Biorelevant dissolution testing and physiologically based absorption modeling to predict in vivo performance of supersaturating drug delivery systems

Supersaturating drug delivery systems (SDDS) enhance the oral absorption of poorly water-soluble drugs by achieving a supersaturated state in the gastrointestinal tract. The maintenance of a supersaturated state is decided by the complex interplay among inherent properties of drug, excipients and physiological conditions of gastrointestinal tract. The biopharmaceutical advantage through SDDS can be mechanistically investigated by coupling biopredictive dissolution testing with physiologically based absorption modeling (PBAM). However, the development of biopredictive dissolution methods possess challenges due to concurrent dissolution, supersaturation, precipitation, and possible redissolution of precipitates during gastrointestinal transit of SDDS. In this comprehensive review, our effort is to critically assess the current state-of-knowledge and provide future directions for PBAM of SDDS. The review outlines various methods used to retrieve physiologically relevant values for input parameters like solubility, dissolution, precipitation, lipid-digestion and permeability of SDDS. SDDS-specific parameterization includes solubility values corresponding to apparent physical form, dissolution in physiologically relevant volumes with biorelevant media, and transfer experiments to incorporate precipitation kinetics. Interestingly, the lack of experimental permeability values and modification of absorption flux through SDDS possess the additional challenge for its PBAM. Supersaturation triggered permeability modifications are reported to fit the observed plasma concentration-time profile. Hence, the experimental insights on good fitting with modified permeability can be potential area of future research for the development of in vitro methods to reliably predict oral absorption of SDDS.

Physiologically-based pharmacokinetic model for alectinib, ruxolitinib, and panobinostat in the presence of cancer, renal impairment, and hepatic impairment

Renal (RIP) and hepatic (HIP) impairments are prevalent conditions in cancer patients. They can cause changes in gastric emptying time, albumin levels, hematocrit, glomerular filtration rate, hepatic functional volume, blood flow rates, and metabolic activity that can modify drug pharmacokinetics. Performing clinical studies in such populations has ethical and practical issues. Using predictive physiologically-based pharmacokinetic (PBPK) models in the evaluation of the PK of alectinib, ruxolitinib, and panobinostat exposures in the presence of cancer, RIP, and HIP can help in using optimal doses with lower toxicity in these populations. Verified PBPK models were customized under scrutiny to account for the pathophysiological changes induced in these diseases. The PBPK model-predicted plasma exposures in patients with different health conditions within average 2-fold error. The PBPK model predicted an area under the curve ratio (AUCR) of 1, and 1.8, for ruxolitinib and panobinostat, respectively, in the presence of severe RIP. On the other hand, the severe HIP was associated with AUCR of 1.4, 2.9, and 1.8 for alectinib, ruxolitinib, and panobinostat, respectively, in agreement with the observed AUCR. Moreover, the PBPK model predicted that alectinib therapeutic cerebrospinal fluid levels are achieved in patients with non-small cell lung cancer, moderate HIP, and severe HIP at 1-, 1.5-, and 1.8-fold that of healthy subjects. The customized PBPK models showed promising ethical alternatives for simulating clinical studies in patients with cancer, RIP, and HIP. More work is needed to quantify other pathophysiological changes induced by simultaneous affliction by cancer and RIP or HIP.

On Absorption Modeling and Food Effect Prediction of Rivaroxaban, a BCS II Drug Orally Administered as an Immediate-Release Tablet

The present work evaluates the food effect on the absorption of rivaroxaban (Riva), a BCS II drug, from the orally administered commercial immediate-release tablet (Xarelto IR) using physiologically based pharmacokinetic (PBPK) and conventional in vitro-in vivo correlation (IVIVC) models. The bioavailability of Riva upon oral administration of Xarelto IR tablet is reported to exhibit a positive food effect. The PBPK model for Riva was developed and verified using the previously reported in vivo data for oral solution (5 and 10 mg) and Xarelto IR tablet (5 and 10 mg dose strength). Once the PBPK model was established, the in vivo performance of the tablet formulation with the higher dose strength (Xarelto IR tablet 20 mg in fasted and fed state) was predicted using the experimentally obtained data of in vitro permeability, biorelevant solubility and in vitro dynamic dissolution data using United States Pharmacopeia (USP) IV flow-through cell apparatus. In addition, the mathematical IVIVC model was developed using the in vitro dissolution and in vivo profile of 20 mg strength Xarelto IR tablet in fasted condition. Using the developed IVIVC model, the pharmacokinetic (PK) profile of the Xarelto IR tablet in fed condition was predicted and compared with the PK parameters obtained via the PBPK model. A virtual in vivo PK study was designed using a single-dose, 3-treatment cross-over trial in 50 subjects to predict the PK profile of the Xarelto® IR tablet in the fed state. Overall, the results obtained from the IVIVC model were found to be comparable with those from the PBPK model. The outcome from both models pointed to the positive food effect on the in vivo profile of the Riva. The developed models thus can be effectively extended to establish bioequivalence for the marketed and novel complex formulations of Riva such as amorphous solid dispersions.

Modeling and Simulation of Process Technology for Nanoparticulate Drug Formulations-A Particle Technology Perspective

Crystalline organic nanoparticles and their amorphous equivalents (ONP) have the potential to become a next-generation formulation technology for dissolution-rate limited biopharmaceutical classification system (BCS) class IIa molecules if the following requisites are met: (i) a quantitative understanding of the bioavailability enhancement benefit versus established formulation technologies and a reliable track record of successful case studies are available; (ii) efficient experimentation workflows with a minimum amount of active ingredient and a high degree of digitalization via, e.g., automation and computer-based experimentation planning are implemented; (iii) the scalability of the nanoparticle-based oral delivery formulation technology from the lab to manufacturing is ensured. Modeling and simulation approaches informed by the pharmaceutical material science paradigm can help to meet these requisites, especially if the entire value chain from formulation to oral delivery is covered. Any comprehensive digitalization of drug formulation requires combining pharmaceutical materials science with the adequate formulation and process technologies on the one hand and quantitative pharmacokinetics and drug administration dynamics in the human body on the other hand. Models for the technical realization of the drug production and the distribution of the pharmaceutical compound in the human body are coupled via the central objective, namely bioavailability. The underlying challenges can only be addressed by hierarchical approaches for property and process design. The tools for multiscale modeling of the here-considered particle processes (e.g., by coupled computational fluid dynamics, population balance models, Noyes–Whitney dissolution kinetics) and physiologically based absorption modeling are available. Significant advances are being made in enhancing the bioavailability of hydrophobic compounds by applying innovative solutions. As examples, the predictive modeling of anti-solvent precipitation is presented, and options for the model development of comminution processes are discussed.

A workflow to build PBTK models for novel species

Physiology-based pharmacokinetic and toxicokinetic (PBPK/TK) models allow us to simulate the concentration of xenobiotica in the plasma and different tissues of an organism. PBPK/TK models are therefore routinely used in many fields of life sciences to simulate the physiological concentration of exogenous compounds in plasma and tissues. The application of PBTK models in ecotoxicology, however, is currently hampered by the limited availability of models for focal species. Here, we present a best practice workflow that describes how to build PBTK models for novel species. To this end, we extrapolated eight previously established rabbit models for several drugs to six additional mammalian species (human, beagle, rat, monkey, mouse, and minipig). We used established PBTK models for these species to account for the species-specific physiology. The parameter sensitivity in the resulting 56 PBTK models was systematically assessed to rank the relevance of the parameters on overall model performance. Interestingly, more than 80% of the 609 considered model parameters showed a negligible sensitivity throughout all models. Only approximately 5% of all parameters had a high sensitivity in at least one of the PBTK models. This approach allowed us to rank the relevance of the various parameters on overall model performance. We used this information to formulate a best practice guideline for the efficient development of PBTK models for novel animal species. We believe that the workflow proposed in this study will significantly support the development of PBTK models for new animal species in the future.

Physiologically based pharmacokinetic-pharmacodynamic modeling for prediction of vonoprazan pharmacokinetics and its inhibition on gastric acid secretion following intravenous/oral administration to rats, dogs and humans

Vonoprazan is characterized as having a long-lasting antisecretory effect on gastric acid. In this study we developed a physiologically based pharmacokinetic (PBPK)-pharmacodynamic (PD) model linking to stomach to simultaneously predict vonoprazan pharmacokinetics and its antisecretory effects following administration to rats, dogs, and humans based on in vitro parameters. The vonoprazan disposition in the stomach was illustrated using a limited-membrane model. In vitro metabolic and transport parameters were derived from hepatic microsomes and Caco-2 cells, respectively. We found the most predicted plasma concentrations and pharmacokinetic parameters of vonoprazan in rats, dogs and humans were within twofold errors of the observed data. Free vonoprazan concentrations (f_u × C₂) in the stomach were simulated and linked to the antisecretory effects of the drug (I) (increases in pH or acid output) using the fomula dI/dt = k × f_u × C₂ × (I_max − I) − k_d × I. The vonoprazan dissociation rate constant k_d (0.00246 min⁻¹) and inhibition index K_I (35 nM) for H⁺/K⁺-ATPase were obtained from literatures. The vonoprazan-H⁺/K⁺-ATPase binding rate constant k was 0.07028 min⁻¹· μM⁻¹ using ratio of k_d to K_I. The predicted antisecretory effects were consistent with the observations following intravenous administration to rats (0.7 and 1.0 mg/kg), oral administration to dogs (0.3 and 1.0 mg/kg) and oral single dose or multidose to humans (20, 30, and 40 mg). Simulations showed that vonoprazan concentrations in stomach were 1000-fold higher than those in the plasma at 24 h following administration to human. Vonoprazan pharmacokinetics and its antisecretory effects may be predicted from in vitro data using the PBPK-PD model of the stomach. These findings may highlight 24-h antisecretory effects of vonoprazan in humans following single-dose or the sustained inhibition throughout each 24-h dosing interval during multidose administration.

Progress in the Mechanism and Clinical Application of Cilostazol

Cilostazol is a unique platelet inhibitor that has been used clinically for more than 20 years. As a phosphodiesterase type III inhibitor, cilostazol is capable of reversible inhibition of platelet aggregation and vasodilation, has antiproliferative effects, and is widely used in the treatment of peripheral arterial disease, cerebrovascular disease, percutaneous coronary intervention, etc. This article briefly reviews the pharmacological mechanisms and clinical application of cilostazol.

A chimpanzee adenoviral vector-based rabies vaccine protects beagle dogs from lethal rabies virus challenge

Rabies continues to poses serious threats to the public health in many countries. The development of novel inexpensive, safe and effective vaccines has become a high priority for rabies control worldwide. We previously generated a novel recombinant rabies vaccine by cloning rabies virus glycoprotein into a chimpanzee adenoviral vector, termed ChAd68-Gp. The present study evaluated the immune responses and protection afforded by this vaccine in beagle dogs. The results demonstrated that intramuscular immunization with both low-dose and high-dose of ChAd68-Gp induced strong immune responses and provided complete protection in beagles even at low-dose. However, when administered orally, high-dose vaccination was protective while low-dose vaccination was ineffective. Further investigation indicated that the low-pH value of gastric juice in the stomach of beagles might decompose the adenovirus. Therefore, suitable formulation for adenovirus-based oral vaccine should be considered and developed. The chimpanzee adenovirus-vectored rabies vaccine ChAd68-Gp warrants extensive test for clinical application.

Double controlled release of highly insoluble cilostazol using surfactant-driven pH dependent and pH-independent polymeric blends and in vivo bioavailability in beagle dogs

Commercially available cilostazol (CIL) tablet releases drug immediately and is given twice a day as an antiplatelet and vasodilatory agent. However, clinical usefulness of immediate release (IR) preparation is limited due to its extremely poor water solubility and the difficulty in sustaining the blood concentration, resulting in unwanted side effects such as headaches, pyknocardia and heavy-headed symptoms. To achieve once a day dosage form with enhanced solubility and controlled release, double controlled release CIL matrix tablets (DCRT) were designed by modulating a sol-gel process of binary polymeric blends of a pH-independent hydroxylpropylmethylcellulose (HPMC) and a pH-dependent polymer (carbomer) assisted with anionic surfactant (sodium lauryl sulfate, SLS). The release profiles of the DCRT were varied according to the ratio of the two polymers. This DCRT enhanced dissolution rate of CIL in a controlled manner due to the sol-gel and erosion process of HPMC, and SLS-driven modulation of charged carbomer via neutralization and micellar interaction. The near-infrared (NIR) chemical imaging and gravimetric behaviors of DCRT clearly showed dynamic modulation of CIL during the swelling and hydration process. Furthermore, the plasma concentration of CIL in DCRT was highly improved and sustained in beagle dogs in a controlled manner.

Effect of finasteride particle size reduction on its pharmacokinetic, tissue distribution and cellular permeation

Finasteride (FSD), a specific competitive inhibitor of the steroid type-II 5α-reductase enzyme, is used in treatment of benign prostate hyperplasia (BPH) and male pattern baldness. The drug is of limited solubility that affect its dissolution and bioavailability. The aim was to study the effect of FSD particle size reduction on the pharmacokinetic, tissue distribution and cellular permeation. An optimized drug micro- and nano-particles were developed, characterized, administered to group of rats, and systemic pharmacokinetic and tissue distribution within target and not-target organs were determined using near-infrared (NIR) spectroscopy technique. Moreover, the cellular permeation of the prepared formulations through normal prostate epithelial cells was assessed and compared to pure FSD. The developed micro- and nano-particles were of 930 and 645 nm, respectively. Plasma maximum drug levels (C_max) and overall exposure (AUC) of both formulations were not significantly higher than unformulated drug. However, micronized FSD achieved significant higher concentration within the target tissue (prostate) within the current study compared to pure drug and nano-sized formulation as well. Yet, this is explained by the higher sequestration ability of spleen tissue to the nano-sized formula compared to micro-sized FSD. At the cellular level, permeation of nano-sized FSD through prostate epithelial cells was superior to the unformulated FSD as well as the micro-sized drug formulation. FSD particle size reduction significantly influences its cellular permeation and to a lesser extend affect its systemic pharmacokinetics and tissue distribution after oral administration.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 2: An introduction to the simulation exercise and overview of results

Orally administered drugs are subject to a number of barriers impacting bioavailability (F_oral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp®, and GastroPlus™) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters. Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details. 43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.). Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, F_oral and relative AUC (F_rel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations. A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.

Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model

The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment.

Development of a paediatric population-based model of the pharmacokinetics of rivaroxaban

Background

Venous thromboembolism has been increasingly recognised as a clinical problem in the paediatric population. Guideline recommendations for antithrombotic therapy in paediatric patients are based mainly on extrapolation from adult clinical trial data, owing to the limited number of clinical trials in paediatric populations. The oral, direct Factor Xa inhibitor rivaroxaban has been approved in adult patients for several thromboembolic disorders, and its well-defined pharmacokinetic and pharmacodynamic characteristics and efficacy and safety profiles in adults warrant further investigation of this agent in the paediatric population.

Objective

The objective of this study was to develop and qualify a physiologically based pharmacokinetic (PBPK) model for rivaroxaban doses of 10 and 20 mg in adults and to scale this model to the paediatric population (0–18 years) to inform the dosing regimen for a clinical study of rivaroxaban in paediatric patients.

Methods

Experimental data sets from phase I studies supported the development and qualification of an adult PBPK model. This adult PBPK model was then scaled to the paediatric population by including anthropometric and physiological information, age-dependent clearance and age-dependent protein binding. The pharmacokinetic properties of rivaroxaban in virtual populations of children were simulated for two body weight-related dosing regimens equivalent to 10 and 20 mg once daily in adults. The quality of the model was judged by means of a visual predictive check. Subsequently, paediatric simulations of the area under the plasma concentration–time curve (AUC), maximum (peak) plasma drug concentration (C_max) and concentration in plasma after 24 h (C_24h) were compared with the adult reference simulations.

Results

Simulations for AUC, C_max and C_24h throughout the investigated age range largely overlapped with values obtained for the corresponding dose in the adult reference simulation for both body weight-related dosing regimens. However, pharmacokinetic values in infants and preschool children (body weight <40 kg) were lower than the 90 % confidence interval threshold of the adult reference model and, therefore, indicated that doses in these groups may need to be increased to achieve the same plasma levels as in adults. For children with body weight between 40 and 70 kg, simulated plasma pharmacokinetic parameters (C_max, C_24h and AUC) overlapped with the values obtained in the corresponding adult reference simulation, indicating that body weight-related exposure was similar between these children and adults. In adolescents of >70 kg body weight, the simulated 90 % prediction interval values of AUC and C_24h were much higher than the 90 % confidence interval of the adult reference population, owing to the weight-based simulation approach, but for these patients rivaroxaban would be administered at adult fixed doses of 10 and 20 mg.

Conclusion

The paediatric PBPK model developed here allowed an exploratory analysis of the pharmacokinetics of rivaroxaban in children to inform the dosing regimen for a clinical study in paediatric patients.

Electronic supplementary material

The online version of this article (doi:10.1007/s40262-013-0090-5) contains supplementary material, which is available to authorized users.

Optimizing nanomedicine pharmacokinetics using physiologically based pharmacokinetics modelling

The delivery of therapeutic agents is characterized by numerous challenges including poor absorption, low penetration in target tissues and non-specific dissemination in organs, leading to toxicity or poor drug exposure. Several nanomedicine strategies have emerged as an advanced approach to enhance drug delivery and improve the treatment of several diseases. Numerous processes mediate the pharmacokinetics of nanoformulations, with the absorption, distribution, metabolism and elimination (ADME) being poorly understood and often differing substantially from traditional formulations. Understanding how nanoformulation composition and physicochemical properties influence drug distribution in the human body is of central importance when developing future treatment strategies. A helpful pharmacological tool to simulate the distribution of nanoformulations is represented by physiologically based pharmacokinetics (PBPK) modelling, which integrates system data describing a population of interest with drug/nanoparticle in vitro data through a mathematical description of ADME. The application of PBPK models for nanomedicine is in its infancy and characterized by several challenges. The integration of property-distribution relationships in PBPK models may benefit nanomedicine research, giving opportunities for innovative development of nanotechnologies. PBPK modelling has the potential to improve our understanding of the mechanisms underpinning nanoformulation disposition and allow for more rapid and accurate determination of their kinetics. This review provides an overview of the current knowledge of nanomedicine distribution and the use of PBPK modelling in the characterization of nanoformulations with optimal pharmacokinetics.

The Biopharmaceutics Classification System: subclasses for in vivo predictive dissolution (IPD) methodology and IVIVC

The Biopharmaceutics Classification System (BCS) has found widespread utility in drug discovery, product development and drug product regulatory sciences. The classification scheme captures the two most significant factors influencing oral drug absorption; solubility and intestinal permeability and it has proven to be a very useful and a widely accepted starting point for drug product development and drug product regulation. The mechanistic base of the BCS approach has, no doubt, contributed to its wide spread acceptance and utility. Nevertheless, underneath the simplicity of BCS are many detailed complexities, both in vitro and in vivo which must be evaluated and investigated for any given drug and drug product. In this manuscript we propose a simple extension of the BCS classes to include sub-specification of acid (a), base (b) and neutral (c) for classes II and IV. Sub-classification for Classes I and III (high solubility drugs as currently defined) is generally not needed except perhaps in border line solubility cases. It is well known that the , pKa physical property of a drug (API) has a significant impact on the aqueous solubility dissolution of drug from the drug product both in vitro and in vivo for BCS Class II and IV acids and bases, and is the basis, we propose for a sub-classification extension of the original BCS classification. This BCS sub-classification is particularly important for in vivo predictive dissolution methodology development due to the complex and variable in vivo environment in the gastrointestinal tract, with its changing pH, buffer capacity, luminal volume, surfactant luminal conditions, permeability profile along the gastrointestinal tract and variable transit and fasted and fed states. We believe this sub-classification is a step toward developing a more science-based mechanistic in vivo predictive dissolution (IPD) methodology. Such a dissolution methodology can be used by development scientists to assess the likelihood of a formulation and dosage form functioning as desired in humans, can be optimized along with parallel human pharmacokinetic studies to set a dissolution methodology for Quality by Design (QbD) and in vitro-in vivo correlations (IVIVC) and ultimately can be used as a basis for a dissolution standard that will ensure continued in vivo product performance.

PBPK models for the prediction of in vivo performance of oral dosage forms

Drug absorption from the gastrointestinal (GI) tract is a highly complex process dependent upon numerous factors including the physicochemical properties of the drug, characteristics of the formulation and interplay with the underlying physiological properties of the GI tract. The ability to accurately predict oral drug absorption during drug product development is becoming more relevant given the current challenges facing the pharmaceutical industry. Physiologically-based pharmacokinetic (PBPK) modeling provides an approach that enables the plasma concentration-time profiles to be predicted from preclinical in vitro and in vivo data and can thus provide a valuable resource to support decisions at various stages of the drug development process. Whilst there have been quite a few successes with PBPK models identifying key issues in the development of new drugs in vivo, there are still many aspects that need to be addressed in order to maximize the utility of the PBPK models to predict drug absorption, including improving our understanding of conditions in the lower small intestine and colon, taking the influence of disease on GI physiology into account and further exploring the reasons behind population variability. Importantly, there is also a need to create more appropriate in vitro models for testing dosage form performance and to streamline data input from these into the PBPK models. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the current status of PBPK models available. The current challenges in PBPK set-ups for oral drug absorption including the composition of GI luminal contents, transit and hydrodynamics, permeability and intestinal wall metabolism are discussed in detail. Further, the challenges regarding the appropriate integration of results from in vitro models, such as consideration of appropriate integration/estimation of solubility and the complexity of the in vitro release and precipitation data, are also highlighted as important steps to advancing the application of PBPK models in drug development. It is expected that the "innovative" integration of in vitro data from more appropriate in vitro models and the enhancement of the GI physiology component of PBPK models, arising from the OrBiTo project, will lead to a significant enhancement in the ability of PBPK models to successfully predict oral drug absorption and advance their role in preclinical and clinical development, as well as for regulatory applications.

Utilizing in vitro and PBPK tools to link ADME characteristics to plasma profiles: case example nifedipine immediate release formulation

One of the most prominent food-drug interactions is the inhibition of intestinal cytochrome P450 (CYP) 3A enzymes by grapefruit juice ingredients, and, as many drugs are metabolized via CYP 3A, this interaction can be of clinical importance. Calcium channel-blocking agents of the dihydropyridine type, such as felodipine and nifedipine, are subject to extensive intestinal first pass metabolism via CYP 3A, thus resulting in significantly enhanced in vivo exposure of the drug when administered together with grapefruit juice. Physiologically based pharmacokinetic (PBPK) modeling was used to simulate pharmacokinetics of a nifedipine immediate release formulation following concomitant grapefruit juice ingestion, that is, after inhibition of small intestinal CYP 3A enzymes. For this purpose, detailed data about CYP 3A levels were collected from the literature and implemented into commercial PBPK software. As literature reports show that grapefruit juice (i) leads to a marked delay in gastric emptying, and (ii) rapidly lowers the levels of intestinal CYP 3A enzymes, inhibition of intestinal first pass metabolism following ingestion of grapefruit juice was simulated by altering the intestinal CYP 3A enzyme levels and simultaneously decelerating the gastric emptying rate. To estimate the in vivo dispersion and dissolution behavior of the formulation, dissolution tests in several media simulating both the fasted and fed state stomach and small intestine were conducted, and the results from the in vitro dissolution tests were used as input function to describe the in vivo dissolution of the drug. Plasma concentration-time profiles of the nifedipine immediate release formulation both with and without simultaneous CYP 3A inhibition were simulated, and the results were compared with data gathered from the literature. Using this approach, nifedipine plasma profiles could be simulated well both with and without enzyme inhibition. A reduction in small intestinal CYP 3A levels by 60% was found to yield the best results, with simulated nifedipine concentration-time profiles within 20% of the in vivo observed results. By additionally varying the dissolution input of the PBPK model, a link between the dissolution characteristics of the formulation and its in vivo performance could be established.

A detailed physiologically based model to simulate the pharmacokinetics and hormonal pharmacodynamics of enalapril on the circulating endocrine Renin-Angiotensin-aldosterone system

The renin-angiotensin-aldosterone system (RAAS) plays a key role in the pathogenesis of cardiovascular disorders including hypertension and is one of the most important targets for drugs. A whole body physiologically based pharmacokinetic (wb PBPK) model integrating this hormone circulation system and its inhibition can be used to explore the influence of drugs that interfere with this system, and thus to improve the understanding of interactions between drugs and the target system. In this study, we describe the development of a mechanistic RAAS model and exemplify drug action by a simulation of enalapril administration. Enalapril and its metabolite enalaprilat are potent inhibitors of the angiotensin-converting-enzyme (ACE). To this end, a coupled dynamic parent-metabolite PBPK model was developed and linked with the RAAS model that consists of seven coupled PBPK models for aldosterone, ACE, angiotensin 1, angiotensin 2, angiotensin 2 receptor type 1, renin, and prorenin. The results indicate that the model represents the interactions in the RAAS in response to the pharmacokinetics (PK) and pharmacodynamics (PD) of enalapril and enalaprilat in an accurate manner. The full set of RAAS-hormone profiles and interactions are consistently described at pre- and post-administration steady state as well as during their dynamic transition and show a good agreement with literature data. The model allows a simultaneous representation of the parent-metabolite conversion to the active form as well as the effect of the drug on the hormone levels, offering a detailed mechanistic insight into the hormone cascade and its inhibition. This model constitutes a first major step to establish a PBPK-PD-model including the PK and the mode of action (MoA) of a drug acting on a dynamic RAAS that can be further used to link to clinical endpoints such as blood pressure.

Integrating cellular metabolism into a multiscale whole-body model

Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development.

Cellular metabolism is a key element in human physiology. Ideally the metabolic network needs to be considered within the context of the surrounding tissue and organism since the various levels of biological organization are mutually influencing each other. To mechanistically describe the interplay between intracellular space and extracellular environment, we here integrate the genome-scale metabolic network model HepatoNet1 at the cellular scale into physiologically-based pharmacokinetic models at the whole-body level. The resulting multiscale model allows the quantitative description of metabolic behavior in the context of time-resolved metabolite concentration profiles in the body and the surrounding liver tissue. The model has been applied to three case studies covering fundamental aspects of medicine and pharmacology: drug administration, biomarker identification and drug-induced toxication. Most notably, our multiscale approach fosters an improved quantitative understanding of drug action and the impact of metabolic disorders at an organism level, based on a genome-scale representation of cellular metabolism. Computational models such as the one presented include various aspects of human physiology and may therefore significantly support rational approaches in medical diagnostics and pharmaceutical drug development in the future.

Drug nanocrystals in the commercial pharmaceutical development process

Nanosizing is one of the most important drug delivery platform approaches for the commercial development of poorly soluble drug molecules. The research efforts of many industrial and academic groups have resulted in various particle size reduction techniques. From an industrial point of view, the two most advanced top-down processes used at the commercial scale are wet ball milling and high pressure homogenization. Initial issues such as abrasion, long milling times and other downstream-processing challenges have been solved. With the better understanding of the biopharmaceutical aspects of poorly water-soluble drugs, the in vivo success rate for drug nanocrystals has become more apparent. The clinical effectiveness of nanocrystals is proven by the fact that there are currently six FDA approved nanocrystal products on the market. Alternative approaches such as bottom-up processes or combination technologies have also gained considerable interest. Due to the versatility of nanosizing technology at the milligram scale up to production scale, nanosuspensions are currently used at all stages of commercial drug development, Today, all major pharmaceutical companies have realized the potential of drug nanocrystals and included this universal formulation approach into their decision trees.

The use of modeling tools to drive efficient oral product design

Modeling and simulation of drug dissolution and oral absorption has been increasingly used over the last decade to understand drug behavior in vivo based on the physicochemical properties of Active Pharmaceutical Ingredients (API) and dosage forms. As in silico and in vitro tools become more sophisticated and our knowledge of physiological processes has grown, model simulations can provide a valuable confluence, tying-in in vitro data with in vivo data while offering mechanistic insights into clinical performance. To a formulation scientist, this unveils not just the parameters that are predicted to significantly impact dissolution/absorption, but helps probe explanations around drug product performance and address specific in vivo mechanisms. In formulation, development, in silico dissolution-absorption modeling can be effectively used to guide: API selection (form comparison and particle size properties), influence clinical study design, assess dosage form performance, guide strategy for dosage form design, and breakdown clinically relevant conditions on dosage form performance (pH effect for patients on pH-elevating treatments, and food effect). This minireview describes examples of these applications in guiding product development including those with strategies to mitigate observed clinical exposure liability or mechanistically probe product in vivo performance attributes.

Integration of dissolution into physiologically-based pharmacokinetic models III: PK-Sim®

OBJECTIVES

In-silico methods are a cost-effective possibility to support decision making at different stages of the drug development process. Among the various computational methods available, physiologically-based pharmacokinetic (PBPK) modelling represents a well-established tool for mechanistically predicting the pharmacokinetics of drugs and drug candidates. PK-Sim, a component of the Computational Systems Biology Software Suite of Bayer Technology Services GmbH (Leverkusen, Germany) is a commercial PBPK software tool. It is based on a generic model structure for typical animal species from mice to monkey and humans, and allows simultaneous simulation of drug liberation, absorption, distribution, metabolism, and excretion in one model. In this study PK-Sim has been used for the prediction of the in-vivo pharmacokinetics of drugs with a particular focus on the integration of dissolution properties and, due to its leading role in the drug development process, for the performance of different dosage forms administered via the oral route.

METHODS

Three real life case studies have been presented to exemplify the benefits of using PBPK absorption modelling.

KEY FINDINGS

In the first example, the in-vivo dissolution rate was directly predicted from the physical properties of different particle formulations using a mechanistic dissolution model of the Noyes-Whitney type. In the second case study, the PBPK tool was successfully used to predict the food effect in humans based on data obtained in Beagle dogs. In the third example, the utilization of the software for the support of the development of a combined immediate release-controlled release formulation has been described.

CONCLUSIONS

Future perspectives of the use of PBPK modelling have been discussed, with a special focus on the integration of in-vitro dissolution data into PBPK models for oral and non-oral administration of drugs.

Analytical methods for dissolution testing of nanosized drugs

OBJECTIVES

This mini-review describes the theoretical advantages of nanosizing drugs in terms of dissolution and the characterization of their behaviour with in-vitro dissolution testing.

KEY FINDINGS

It is shown that the increase in dissolution rate is not commensurate with common theories. The calculation of dissolution rate by surface area increase using the Nernst-Brunner equation is inappropriate since the diffusion layer, δ, cannot be assessed. These results highlight the importance of an appropriate experimental design to assess the dissolution rate in vitro, which will then serve as a building block for establishing in vitro-in vivo correlations. Several techniques to assess the amount of released drug in dissolution testing are discussed, some through a review of current literature (dialysis, turbidity measurement methods, fibre optics, asymmetrical flow-field-flow fractionation), some through experimental experience (ion-selective electrode and syringe filters). Further methods, such as microdialysis, ultrasonic resonance technology and centrifugal filter devices, are reviewed from literature with some additional data obtained in house. The techniques are further discussed with a view to coupling the results with simulation software tools such as STELLA© to predict the in-vivo behaviour of the drug. In doing so, it is necessary to generate experimental data on the dissolution rate, since this cannot be calculated directly from the surface increase of drug particles but rather depends on further factors such as the boundary layer thickness.

SUMMARY

It was concluded that syringe filters of appropriate pore size and the ion-selective electrode appear to be suitable for measurement of the dissolution rate of nanosized drugs.

Prediction of a potentially effective dose in humans for BAY 60-5521, a potent inhibitor of cholesteryl ester transfer protein (CETP) by allometric species scaling and combined pharmacodynamic and physiologically-based pharmacokinetic modelling

AIMS

The purpose of this work was to support the prediction of a potentially effective dose for the CETP-inhibitor, BAY 60-5521, in humans.

METHODS

A combination of allometric scaling of the pharmacokinetics of the CETP-inhibitor BAY 60-5521 with pharmacodynamic studies in CETP-transgenic mice and in human plasma with physiologically-based pharmacokinetic (PBPK) modelling was used to support the selection of the first-in-man dose.

RESULTS

The PBPK approach predicts a greater extent of distribution for BAY 60-5521 in humans compared with the allometric scaling method as reflected by a larger predicted volume of distribution and longer elimination half-life. The combined approach led to an estimate of a potentially effective dose for BAY 60-5521 of 51 mg in humans.

CONCLUSION

The approach described in this paper supported the prediction of a potentially effective dose for the CETP-inhibitor BAY 60-5521 in humans. Confirmation of the dose estimate was obtained in a first-in-man study.

Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling

Active processes involved in drug metabolization and distribution mediated by enzymes, transporters, or binding partners mostly occur simultaneously in various organs. However, a quantitative description of active processes is difficult because of limited experimental accessibility of tissue-specific protein activity in vivo. In this work, we present a novel approach to estimate in vivo activity of such enzymes or transporters that have an influence on drug pharmacokinetics. Tissue-specific mRNA expression is used as a surrogate for protein abundance and activity and is integrated into physiologically based pharmacokinetic (PBPK) models that already represent detailed anatomical and physiological information. The new approach was evaluated using three publicly available databases: whole-genome expression microarrays from ArrayExpress, reverse transcription-polymerase chain reaction-derived gene expression estimates collected from the literature, and expressed sequence tags from UniGene. Expression data were preprocessed and stored in a customized database that was then used to build PBPK models for pravastatin in humans. These models represented drug uptake by organic anion-transporting polypeptide 1B1 and organic anion transporter 3, active efflux by multidrug resistance protein 2, and metabolization by sulfotransferases in liver, kidney, and/or intestine. Benchmarking of PBPK models based on gene expression data against alternative models with either a less complex model structure or randomly assigned gene expression values clearly demonstrated the superior model performance of the former. Besides accurate prediction of drug pharmacokinetics, integration of relative gene expression data in PBPK models offers the unique possibility to simultaneously investigate drug-drug interactions in all relevant organs because of the physiological representation of protein-mediated processes.

Mathematical modeling of simultaneous drug release and in vivo absorption

The attention of this review is focussed on the mathematical modeling of the simultaneous processes of drug release and absorption/distribution/metabolism/elimination (ADME processes) following different administration routes. Among all of them, for their clinical importance, the oral, transdermal and local delivery are considered. The bases of the presented mathematical models are shown after the discussion of the most relevant phenomena characterising the particular administration route considered. Then, model performances are compared to experimental evidences in order to evaluate their reliability and soundness. The most important conclusion of this review is that despite the complexity of the problem involved in the description of the fate of the drugs after their administration, the scientific community is close to the solution as witnessed by the various interesting and promising approaches here presented about the oral, transdermal and local administration routes.