State of the Art and Uses for the Biopharmaceutics Drug Disposition Classification System (BDDCS): New Additions, Revisions, and Citation References

Mechanisms of intestinal pharmacokinetic natural product-drug interactions

The growing co-consumption of botanical natural products with conventional medications has intensified the need to understand potential effects on drug safety and efficacy. This review delves into the intricacies of intestinal pharmacokinetic interactions between botanical natural products and drugs, such as alterations in drug solubility, permeability, transporter activity, and enzyme-mediated metabolism. It emphasizes the importance of understanding how drug solubility, dissolution, and osmolality interplay with botanical constituents in the gastrointestinal tract, potentially altering drug absorption and systemic exposure. Unlike reviews that focus primarily on enzyme and transporter mechanisms, this article highlights the lesser known but equally important mechanisms of interaction. Applying the Biopharmaceutics Drug Disposition Classification System (BDDCS) can serve as a framework for predicting and understanding these interactions. Through a comprehensive examination of specific botanical natural products such as byakkokaninjinto, green tea catechins, goldenseal, spinach extract, and quercetin, we illustrate the diversity of these interactions and their dependence on the physicochemical properties of the drug and the botanical constituents involved. This understanding is vital for healthcare professionals to effectively anticipate and manage potential natural product-drug interactions, ensuring optimal patient therapeutic outcomes. By exploring these emerging mechanisms, we aim to broaden the scope of natural product-drug interaction research and encourage comprehensive studies to better elucidate complex mechanisms.

In vivo deposition of poorly soluble drugs

Administered drug molecules, whether dissolved or solubilized, have the potential to precipitate and accumulate as solid forms in tissues and cells within the body. This phase transition can significantly impact the pharmacokinetics of treatment. It is thus crucial to gain an understanding of how drug solubility/permeability, drug formulations and routes of administration affect in vivo behaviors of drug deposition. This review examines literature reports on the drug deposition in tissues and cells of poorly water-soluble drugs, as well as underlying physical mechanisms that lead to precipitation. Our work particularly highlights drug deposition in macrophages and the subcellular fate of precipitated drugs. We also propose a tissue permeability-based classification framework to evaluate precipitation potentials of poorly soluble drugs in major organs and tissues. The impact on pharmacokinetics is further discussed and needs to be considered in developing drug delivery systems. Finally, bioimaging techniques that are used to examine aggregated states and the intracellular trafficking of absorbed drugs are summarized.

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

Prediction of the First-Pass Metabolism of a Drug After Oral Intake Based on Structural Parameters and Physicochemical Properties

BACKGROUND AND OBJECTIVE

The oral first-pass metabolism is a crucial factor that plays a key role in a drug's pharmacokinetic profile. Prediction of the oral first-pass metabolism based on chemical structural parameters can be useful in the drug-design process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary to reduce the cost and time associated with evaluating the extent of the first-pass metabolism of a candidate compound in preclinical studies. The aim of this study is to estimate the first-pass metabolism of an orally administered drug.

METHODS

A set of compounds with reported first-pass metabolism data were collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system that split the drugs up based on their first-pass metabolism extents. Various structural parameters were calculated for each compound. The relations of the structural and physicochemical values of each compound to the class the compound belongs to were obtained using logistic regression.

RESULTS

Initial analysis showed that compounds with logD7.4 > 1 or a rugosity factor of > 1.5 are more likely to have high first-pass metabolism. Four different models that can predict the oral first-pass metabolism with acceptable error were introduced. The overall accuracies of the models were in the range of 72% (for models with simple descriptors) to 78% (for models with complex descriptors). Although the models with simple descriptors have lower accuracies compared to complex models, they are more interpretable and easier for researchers to utilize.

CONCLUSION

A novel classification of drugs based on the extent of the oral first-pass metabolism was introduced, and mechanistic models were developed to assign candidate compounds to the appropriate proposed classes.

Effect of food on oral pharmacokinetics of edaravone coamorphous dispersion containing bile salts as coformers - Part II

OBJECTIVES

Edaravone (EDR) is an effective neuroprotective agent in various neurological diseases; however, its use is restricted due to poor oral absorption. Bile salts are known for improving solubility and inhibiting drug crystallization in supersaturated conditions of the gastrointestinal tract (GIT). In our previous work, we prepared coamorphous dispersion (COAM) of EDR with sodium taurocholate (NaTC) using spray drying. The optimized EDR COAM exhibited superior in vitro performance compared to plain EDR. EDR is well absorbed in fasted-over-fed conditions.

METHODS

The present work, we conducted a pharmacokinetic study for EDR and EDR COAM in fasted and fed conditions to check effect of food on its oral absorption. The LC-MS/MS-based method was developed and validated to determine the amount of EDR in plasma.

RESULTS

The results suggested that EDR COAM did not show a significant difference in Cmax (P=0.3544) and AUC (P=0.1696) of fasted and fed states. On the other hand, plain EDR showed 2-fold and 3-fold reduced Cmax (P<0.0001) and AUC (P=0.0094) in the fed condition, respectively. The Cmax and AUC of EDR COAM were improved in fasted (AUC: 2.56-fold) and fed states (AUC: 5.74-fold) than plain EDR, suggesting better oral absorption of COAM than crystalline EDR without having the effect of food.

CONCLUSIONS

The unique structural attributes of NaTC had the potential to inhibit the recrystallization of EDR in GIT, while concurrently reducing the impact of food on the oral absorption of EDR.

Doses Evaluated in Clinical Pharmacology Studies Investigating the Effect of Intrinsic and Extrinsic Factors on PK and Safety: Case Examples from Approved Drug Development Programs

Dose selection for investigations of intrinsic and extrinsic factors of pharmacokinetic variability as well as safety is a challenging question in the early clinical stage of drug development. The dose of an investigational product is chosen considering the compound information available to date, feasibility of the assessments, regulatory requirements, and the intent to maximize information for later regulatory submission. This review selected 37 programs as case examples of recently approved drugs to explore the doses selected with focus on studies of drug interaction, renal and hepatic impairment, food effect and concentration-QTc assessment.The review found that regulatory agencies may consider alternative approaches if justified and safe as illustrated in these examples. It is thus recommendable to use the first in human trial as an opportunity to assess QT-prolongation and drug interactions using probes or endogenous markers while maximizing the DDI potential, increasing sensitivity and ensuring safety. Early understanding of dose proportionality assists dose finding and simple and fast to conduct DDI study designs are advantageous. Single dose impairment studies despite non-proportional/time-dependent PK are often acceptability.Overall, the early understanding of the drug's safety profile is essential to ensure the safety of doses selected while preventing clinical trials with unnecessary exposure when using high doses or multiple doses. The information collected in this retrospective survey is a good reminder to tailor the early clinical program to the profile and needs of the molecule and consider regulatory opportunities to streamline the development path.

Drug-induced liver injury associated with elexacaftor/tezacaftor/ivacaftor: A pharmacovigilance analysis of the FDA adverse event reporting system (FAERS)

INTRODUCTION

The efficacy and safety of elexacaftor/tezacaftor/ivacaftor (ETI) have been established in prospective clinical trials. Liver function test elevations were observed in a greater proportion of patients receiving ETI compared with placebo; however, the relatively small number of patients and short duration of study preclude detection of rare but clinically significant associations with drug-induced liver injury (DILI). To address this gap, we assessed the real-world risk of DILI associated with ETI through data mining of the FDA adverse event reporting system (FAERS).

METHODS

Disproportionality analyses were conducted on FAERS data from the fourth quarter of 2019 through the third quarter of 2022. Comparative patient demographics, onset time and outcomes for ETI-DILI were also obtained.

RESULTS

452 reports of DILI associated with ETI were found, representing 2.1 % of all adverse event reports for ETI. All disproportionality measures were significant for ETI-DILI at p < 0.05; the reporting odds ratio (ROR) (2.82) was comparable to that of drugs classified by FDA as "Most-DILI concern". The most notable demographic finding was a male majority (5:4 male to female ratio) for ETI-DILI compared to a female majority (4:5 male to female ratio) for non ETI-DILI. Median ETI-DILI onset time was 50.5 days, and hospitalization was the second most common complication.

CONCLUSION

Using FAERS data, ETI was found to be disproportionately associated with DILI. Future research is needed to investigate the hepatotoxic mechanisms and assess potential mitigation strategies for ETI-induced hepatotoxicity.

Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity

Drug-induced renal injury (DIRI) causes >1.5 million adverse events annually in the USA alone. Although standard biomarkers exist for DIRI, they lack the sensitivity or specificity to detect nephrotoxicity before the significant loss of renal function. In this study, we describe the creation of DIRIL - a list of drugs associated with DIRI and nephrotoxicity - from two literature datasets with DIRI annotation, confirmed using FDA drug labeling. DIRIL comprises 317 orally administered drugs covering all 14 anatomical, therapeutic and chemical (ATC) classification categories. Of the 317 drugs, 171 were DIRI-positive and 146 were DIRI-negative. DIRIL will be a relevant and invaluable resource for discovery of new approach methods (NAMs) to predict the occurrence and possible severity of DIRI earlier in drug development.

Green Tea Catechins Decrease Solubility of Raloxifene In Vitro and Its Systemic Exposure in Mice

PURPOSE

Green tea is a widely consumed beverage. A recent clinical study reported green tea decreased systemic exposure of raloxifene and its glucuronide metabolites by 34-43%. However, the underlying mechanism(s) remains unknown. This study investigated a change in raloxifene's solubility as the responsible mechanism.

METHODS

The effects of green tea extract, (-)-epigallocatechin gallate (EGCG), and (-)-epigallocatechin (EGC) on raloxifene's solubility were assessed in fasted state simulated intestinal fluids (FaSSIF) and fed state simulated intestinal fluids (FeSSIF). EGCG and EGC represent green tea's main bioactive constituents, flavan-3-gallate and flavan-3-ol catechins respectively, and the tested concentrations (mM) match the µg/mg of each compound in the extract. Our mouse study (n = 5/time point) evaluated the effect of green tea extract and EGCG on the systemic exposure of raloxifene.

RESULTS

EGCG (1 mM) and EGC (1.27 mM) decreased raloxifene's solubility in FaSSIF by 78% and 13%, respectively. Micelle size in FaSSIF increased with increasing EGCG concentrations (> 1000% at 1 mM), whereas EGC (1.27 mM) did not change micelle size. We observed 3.4-fold higher raloxifene solubility in FeSSIF compared to FaSSIF, and neither green tea extract nor EGCG significantly affected raloxifene solubility or micelle size in FeSSIF. The mice study showed that green tea extract significantly decreased raloxifene Cmax by 44%, whereas EGCG had no effect. Green tea extract and EGCG did not affect the AUC0-24 h of raloxifene or the metabolite-to-parent AUC ratio.

CONCLUSIONS

This study demonstrated flavan-3-gallate catechins may decrease solubility of poorly water-soluble drugs such as raloxifene, particularly in the fasted state.

An Explanation of Why Dose-Corrected Area Under the Curve for Alternate Administration Routes Can Be Greater than for Intravenous Dosing

It is generally believed that bioavailability (F) calculated based on systemic concentration area under the curve (AUC) measurements cannot exceed 1.0, yet some published studies report this inconsistency. We teach and believe, based on differential equation derivations, that rate of absorption has no influence on measured systemic clearance following an oral dose, i.e., determined as available dose divided by AUC. Previously, it was thought that any difference in calculating F from urine data versus that from systemic concentration AUC data was due to the inability to accurately measure urine data. A PubMed literature search for drugs exhibiting F > 1.0 and studies for which F was measured using both AUC and urinary excretion dose-corrected analyses yielded data for 35 drugs. We show and explain, using Kirchhoff's Laws, that these universally held concepts concerning bioavailability may not be valid in all situations. Bioavailability, determined using systemic concentration measurements, for many drugs may be overestimated since AUC reflects not only systemic elimination but also absorption rate characteristics, which is most easily seen for renal clearance measures. Clearance of drug from the absorption site must be significantly greater than clearance following an iv bolus dose for F(AUC) to correctly correspond with F(urine). The primary purpose of this paper is to demonstrate that studies resulting in F > 1.0 and/or greater systemic vs urine bioavailability predictions may be accurate. Importantly, these explications have no significant impact on current regulatory guidance for bioequivalence testing, nor on the use of exposure (AUC) measures in making drug dosing decisions.

Neural Network Models for Predicting Solubility and Metabolism Class of Drugs in the Biopharmaceutics Drug Disposition Classification System (BDDCS)

BACKGROUND AND OBJECTIVE

The biopharmaceutics drug disposition classification system (BDDCS) categorizes drugs into four classes on the basis of their solubility and metabolism. This framework allows for the study of the pharmacokinetics of transporters and enzymatic metabolization on biopharmaceuticals, as well as drug-drug interactions in the body. The objective of the present study was to develop computational models by neural network models and structural parameters and physicochemical properties to estimate the class of a drug in the BDDCS system.

METHODS

In this study, deep learning methods were utilized to explore the potential of artificial and convolutional neural networks (ANNs and CNNs) in predicting the BDDCS class of 721 substances. The structural parameters and physicochemical properties [Abraham solvation parameters, octanol-water partition (log P) and over the pH range 1-7.5 (log D), number of rotatable bonds, hydrogen bond acceptor numbers, as well as hydrogen bond donor count] are calculated with various software. These compounds were then split into a training set consisting of 602 molecules and a test set of 119 compounds to validate the models.

RESULTS

The results of this study showed that neural network models using applied parameters of the drug, i.e., log D and Abraham solvation parameters, are able to predict the class of solubility and metabolism in the BDDCS system with good accuracy.

CONCLUSIONS

Neural network models are well equipped to deal with the relations between the structural parameters and physicochemical properties of drugs and BDDCS classes. In addition, log D is a more suitable parameter compared with log P in predicting BDDCS.

Exploring DrugCentral: from molecular structures to clinical effects

DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.

Drug induced liver injury - a 2023 update

Drug-Induced Liver Injury (DILI) constitutes hepatic damage attributed to drug exposure. DILI may be categorized as hepatocellular, cholestatic or mixed and might also involve immune responses. When DILI occurs in dose-dependent manner, it is referred to as intrinsic, while if the injury occurs spontaneously, it is termed as idiosyncratic. This review predominately focused on idiosyncratic liver injury. The established molecular mechanisms for DILI include (1) mitochondria dysfunction, (2) increased reactive oxygen species levels, (3) presence of elevated apoptosis and necrosis, (4) and bile duct injuries associated with immune mediated pathways. However, it should be emphasized that the underlying mechanisms responsible for DILI are still unknown. Prevention strategies are critical as incidences occur frequently, and treatment options are limited once the injury has developed. The aim of this review was to utilize retrospective cohort studies from across the globe to gain insight into epidemiological patterns. This review considers (1) what is currently known regarding the mechanisms underlying DILI, (2) discusses potential risk factors and (3) implications of the coronavirus pandemic on DILI presentation and research. Future perspectives are also considered and discussed and include potential new biomarkers, causality assessment and reporting methods.

Determination of furosemide and its glucuronide metabolite in plasma, plasma ultrafiltrate and urine by HPLC-MS/MS with application to secretion and metabolite formation clearances in non-pregnant and pregnant women

Furosemide (FUR) has been used in probe drugs cocktails for in vivo evaluation of the renal transporters OAT1 and OAT3 activities in studies of drug-drug interactions (generally using probenecid as an inhibitor) and drug-disease interactions. The objective of this study was to develop and validate methods for FUR and its glucuronide metabolite (FUR-GLU) analysis in plasma, plasma ultrafiltrate and urine for application in pharmacokinetics studies: a pilot drug-drug interaction study in pregnant women (n = 2), who received a single oral dose of FUR (40 mg) and in another occasion a single oral dose of probenecid (750 mg) before a single oral dose of FUR (40 mg), and in non-pregnant women participants (n = 12), who only received a single oral dose of FUR (40 mg). The samples preparation for FUR in 50 µL of plasma and plasma lysate were carried by acidified liquid-liquid extraction, while 50 µL of urine and 200 µL of plasma ultrafiltrate were simply diluted with the mobile phase. The methods presented linearities in the range of 0.50 - 2500 ng/mL of plasma and plasma lysate, 0.125 - 250 ng/mL of plasma ultrafiltrate, and 50 - 20,000 ng/mL of urine. FUR-GLU methods presented linearities in the range of 0.125 - 250 ng/mL of plasma ultrafiltrate and 50 - 20,000 ng/mL of urine. Precision and accuracy evaluations showed coefficients of variation and relative errors < 15%. In the pregnant women participants, the mean values of FUR CLrenal, CLsecretion, CLformation. FUR-GLU and CLnon-renal were all reduced when probenecid was administered with FUR (8.24 vs 2.89 L/h, 8.15 vs 2.80 L/h, 3.86 vs 1.75 L/h, 48.26 vs 22.10 L/h, respectively). Non-pregnant women presented similar values of FUR CLrenal, CLsecretion, CLformation. FUR-GLU to the pregnant women who received FUR only. Finally, FUR fraction unbound (fu) resulted in values of approximately 1% in pregnant women and to 0.22% in non-pregnant women. These developed and validated methods for FUR and FUR-GLU quantification in multiple matrices can allow the further investigation of UGT1A9/1A1 and the fu when FUR is administered as an OAT 1 and 3 in vivo probe.

Solubility-Permeability Interplay in Facilitating the Prediction of Drug Disposition Routes, Extent of Absorption, Food Effects, Brain Penetration and Drug Induced Liver Injury Potential

Here I detail the use of measures of permeability rate and solubility in predicting drug disposition characteristics through the utilization of the Biopharmaceutics Drug Disposition Classification System (BDDCS) and the Extended Clearance Classification System (ECCS) as well as the accuracy of the systems in predicting the major route of elimination and the extent of oral absorption of a new small molecule therapeutics. I compare the BDDCS and ECCS with the FDA Biopharmaceutics Classification System (BCS). I also detail the use of the BCS in predicting food effects and the BDDCS in predicting brain disposition of small molecule therapeutics and in validating DILI predictive metrics. This review provides an update of the current status of these classification systems and their uses in the drug development process.

Cracking the intestinal lymphatic system window utilizing oral delivery vehicles for precise therapy

Oral administration is preferred over other drug delivery methods due to its safety, high patient compliance, ease of ingestion without discomfort, and tolerance of a wide range of medications. However, oral drug delivery is limited by the poor oral bioavailability of many drugs, caused by extreme conditions and absorption challenges in the gastrointestinal tract. This review thoroughly discusses the targeted drug vehicles to the intestinal lymphatic system (ILS). It explores the structure and physiological barriers of the ILS, highlighting its significance in dietary lipid and medication absorption and transport. The review presents various approaches to targeting the ILS using spatially precise vehicles, aiming to enhance bioavailability, achieve targeted delivery, and reduce first-pass metabolism with serve in clinic. Furthermore, the review outlines several methods for leveraging these vehicles to open the ILS window, paving the way for potential clinical applications in cancer treatment and oral vaccine delivery. By focusing on targeted drug vehicles to the ILS, this article emphasizes the critical role of these strategies in improving therapeutic efficacy and patient outcomes. Overall, this article emphasizes the critical role of targeted drug vehicles to the ILS and the potential impact of these strategies on improving therapeutic efficacy and patient outcomes.

A Perspective on Model-Informed IVIVC for Development of Subcutaneous Injectables

Subcutaneously administered drugs are growing in popularity for both large and small molecule drugs. However, development of these systems - particularly generics - is slowed due to a lack of formal guidance regarding preclinical testing and in vitro - in vivo correlations (IVIVC). Many of these methods, while appropriate for oral drugs, may not be optimized for the complex injection site physiologies, and release rate and absorption mechanisms of subcutaneous drugs. Current limitations for formulation design and IVIVC can be supported by implementing mechanistic, computational methods. These methods can help to inform drug development by identifying key drug and formulation attributes, and their effects on drug release rates. This perspective, therefore, addresses current guidelines in place for oral IVIVC development, how they may differ for subcutaneously administered compounds, and how modeling and simulation can be implemented to inform design of these products. As such, integration of modeling and simulation with current IVIVC systems can help in driving the development of subcutaneous injectables.

Streamlining Food Effect Assessment - Are Repeated Food Effect Studies Needed? An IQ Analysis

Current regulatory guidelines on drug-food interactions recommend an early assessment of food effect to inform clinical dosing instructions, as well as a pivotal food effect study on the to-be-marketed formulation if different from that used in earlier trials. Study waivers are currently only granted for BCS class 1 drugs. Thus, repeated food effect studies are prevalent in clinical development, with the initial evaluation conducted as early as the first-in-human studies. Information on repeated food effect studies is not common in the public domain. The goal of the work presented in this manuscript from the Food Effect PBPK IQ Working Group was to compile a dataset on these studies across pharmaceutical companies and provide recommendations on their conduct. Based on 54 studies collected, we report that most of the repeat food effect studies do not result in meaningful differences in the assessment of the food effect. Seldom changes observed were more than twofold. There was no clear relationship between the change in food effect and the formulation change, indicating that in most cases, once a compound is formulated appropriately within a specific formulation technology, the food effect is primarily driven by inherent compound properties. Representative examples of PBPK models demonstrate that following appropriate validation of the model with the initial food effect study, the models can be applied to future formulations. We recommend that repeat food effect studies should be approached on a case-by-case basis taking into account the totality of the evidence including the use of PBPK modeling.

Use of surfactant-based amorphous solid dispersions for BDDCS class II drugs to enhance oral bioavailability: A case report of resveratrol

This paper aimed to improve in vitro dissolution/solubility as well as inhibit intestinal metabolism and thus enhance oral bioavailability for a BDDCS class II drug by constructing surfactant-based amorphous solid dispersions using resveratrol (RES) as a model drug. After preliminary screening of polymers and surfactants, and subsequent prescription optimization, two optimized spray-drying RES-polymer-surfactant ASDs were obtained and exhibited a significant increase in solubility of RES by 2.69-3.45-fold compared to crystalline RES, and by 1.13-1.56-fold compared to corresponding RES-polymer ASDs, maintaining a higher concentration in the dissolution process. A metabolism study using everted sacs showed that two optimized ASDs reduced the concentration ratio of RES-G to RES to 51.66%-52.05% of crystalline RES on the serosal side of the rat everted intestinal sac at 2 h. Consequently, these two RES-polymer-surfactant ASDs achieved significantly higher exposure of RES in the plasma with significant enhancements in C_max (2.33-2.35-fold higher than crystalline RES, and 1.72-2.04-fold higher than corresponding RES-polymer ASDs), and in AUC _0-∞ (3.51-3.56-fold higher than crystalline RES, and 1.38-1.41-fold higher than corresponding RES-polymer ASDs). These advantages of the RES-polymer-surfactant ASDs in oral absorption of RES were attributed to solubilization by ASDs and metabolic inhibition by UGT inhibitors. The introduction of surfactants including EL and Lab to ASDs plays an important role in inhibiting glucuronidation and further improving solubility. This study demonstrated that such surfactant-based amorphous solid dispersions may serve as a new approach to increase the oral absorption of BDDCS class II drugs.

Development of physiologically-based gut absorption model for probabilistic prediction of environmental chemical bioavailability

Absorption in the gastrointestinal tract is a key factor determining the bioavailability of chemicals after oral exposure but is frequently assumed to have a conservative value of 100% for environmental chemicals, particularly in the context of high-throughput toxicokinetics for in vitro-to-in vivo extrapolation (IVIVE). For pharmaceutical compounds, the physiologically based advanced compartmental absorption and transit (ACAT) model has been used extensively to predict gut absorption but has not generally been applied to environmental chemicals. Here we develop a probabilistic environmental compart­mental absorption and transit (PECAT) model, adapting the ACAT model to environmental chemicals. We calibrated the model parameters to human in vivo, ex vivo, and in vitro datasets of drug permeability and fractional absorption by con­sidering two key factors: (1) differences between permeability in Caco-2 cells and in vivo permeability in the jejunum, and (2) differences in in vivo permeability across different gut segments. Incorporating these factors probabilistically, we found that given Caco-2 permeability measurements, predictions of the PECAT model are consistent with the (limited) available gut absorption data for environmental chemicals. However, the substantial chemical-to-chemical variability observed in the cal­ibration data often led to wide probabilistic confidence bounds in the predicted fraction absorbed and resulting steady state blood concentration. Thus, while the PECAT model provides a statistically rigorous, physiologically based approach for incor­porating in vitro data on gut absorption into toxicokinetic modeling and IVIVE, it also highlights the need for more accurate in vitro models and data for measuring gut segment-specific in vivo permeability of environmental chemicals.

Involvement of Transporters in Intestinal Drug-Drug Interactions of Oral Targeted Anticancer Drugs Assessed by Changes in Drug Absorption Time

(1) Background: Oral targeted anticancer drugs are victims of presystemic pharmacokinetic drug−drug interactions (DDI). Identification of the nature of these DDIs, i.e., enzyme-based or/and transporter-based, is challenging, since most of these drugs are substrates of intestinal and/or hepatic cytochrome P-450 enzymes and of intestinal membrane transporters. (2) Methods: Variations in mean absorption time (MAT) between DDIs and control period (MAT ratios < 0.77 or >1.30) have been proposed to implicate transporters in DDIs at the intestinal level. This methodology has been applied to a large set of oral targeted anticancer drugs (n = 54, involved in 77 DDI studies), from DDI studies available either in the international literature and/or in publicly accessible FDA files. (3) Results: Significant variations in MAT were evidenced in 33 DDI studies, 12 of which could be explained by modulation of an efflux transporter. In 21 DDI studies, modulation of efflux transporters could not explain the MAT variation, suggesting a possible relevant role of influx transporters in the intestinal absorption. (4) Conclusions: This methodology allows one to suggest the involvement of intestinal transporters in DDIs, and should be used in conjunction with in vitro methodologies to help understanding the origin of DDIs.

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

The Usefulness of In Vitro Percutaneous Absorption Experiments Applying the Infinite Dose Technique to Predict In Vivo Plasma Levels: Comparison of Model-Predicted and Observed Plasma Concentrations of Nortriptyline in Rats

The aims of this study were to evaluate the feasibility of a nortriptyline (NT) formulation for transdermal administration and to assess the usefulness of an estimated kinetic parameter (kout) using the in vitro infinite dose technique to predict in vivo plasma levels when used in combination with pharmacokinetic parameters. To do so, a simple one-compartment model was used to describe the transport of a permeant across a membrane (skin). This model provides relatively simple expressions for the amount of permeant in the skin, the cumulative amount of permeant that crosses the skin, and the flux of permeant, for both the infinite and the finite dose regimens. Transdermal administration of the formulated NT gel to rats resulted in plasma levels of approximately 150 ng/mL between 8 and 30 h post-administration. These levels were higher than the minimum concentration of 40 ng/mL recommended for smoking cessation therapy and slightly higher than the upper limit of the therapeutic range for the treatment of depression in humans. The one-compartment model used to describe transport across the skin was connected to a two-compartment pharmacokinetic model used to predict NT plasma concentrations in rats using the kout determined in vitro and the values of other pharmacokinetic parameters obtained in vivo. The predicted concentrations were close to the observed plasma levels and the time profiles were similar for both types of data. These results show the usefulness of the kout parameter determined in vitro to predict plasma concentrations of drugs administered percutaneously.

Important roles of transporters in the pharmacokinetics of anti-viral nucleoside/nucleotide analogs

INTRODUCTION

Nucleoside analogs are an important class of antiviral agents. Due to the high hydrophilicity and limited membrane permeability of antiviral nucleoside/nucleotide analogs (AVNAs), transporters play critical roles in AVNA pharmacokinetics. Understanding the properties of these transporters is important to accelerate translational research for AVNAs.

AREAS COVERED

The roles of key transporters in the pharmacokinetics of 25 approved AVNAs were reviewed. Clinically relevant information that can be explained by the modulation of transporter functions is also highlighted.

EXPERT OPINION

Although the roles of transporters in the intestinal absorption and renal excretion of AVNAs have been well identified, more research is warranted to understand their roles in the distribution of AVNAs, especially to immune privileged compartments where treatment of viral infection is challenging. P-gp, MRP4, BCRP, and nucleoside transporters have shown extensive impacts in the disposition of AVNAs. It is highly recommended that the role of transporters should be investigated during the development of novel AVNAs. Clinically, co-administered inhibitors and genetic polymorphism of transporters are the two most frequently reported factors altering AVNA pharmacokinetics. Physiopathology conditions also regulate transporter activities, while their effects on pharmacokinetics need further exploration. Pharmacokinetic models could be useful for elucidating these complicated factors in clinical settings.