Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspective

Food-Drug Effects and Pediatric Drug Development Studies Submitted to the US Food and Drug Administration, 2012-2022

Food effect (FE) studies characterize food-drug interactions that may alter the efficacy or safety of a drug, but these studies are not conducted in pediatric patients. Pediatric patients have substantial physiologic, anatomic, and dietary differences from adults, which may result in differences in their FE considerations. Therefore, the objective of this study was to identify oral drug products approved for use in pediatric patients aged <6 years with an FE observed in adults. Additional objectives were to summarize the therapeutic areas, pharmacokinetic effects, and labeling instructions that resulted from these studies. Publicly available data were searched for products studied in pediatric patients and approved for use by the United States Food and Drug Administration (FDA) from 2012 to 2022. Of the 102 oral drug products approved for use in patients aged <6 years, 43 recommended the consideration of food intake in the drug labeling. These included drug products recommended to be taken with food (n = 21, 49%) or without food (n = 14, 33%). Each of the 14 drug products recommended to be taken without food are approved for use in pediatric patients aged <2 years. The products approved for use in pediatric patients aged <2 years comprised the highest proportion with area under the plasma concentration-time curve extrapolated to infinity (AUCinf, n = 35, 75%) and maximum serum concentration (Cmax, n = 45, 80%) affected by food. Close monitoring is warranted during the postapproval period for products identified as having a significant FE in adults and that are approved for use in pediatric patients aged <6 years. Promising tools for predicting pediatric FE may include physiologically based pharmacokinetic absorption modeling.

Tipifarnib physiologically-based pharmacokinetic modeling to assess drug-drug interaction, organ impairment, and biopharmaceutics in healthy subjects and cancer patients

A physiologically-based pharmacokinetic (PBPK) model for tipifarnib, which included mechanistic absorption, was built and verified by integrating in vitro data and several clinical data in healthy subjects and cancer patients. The final PBPK model was able to recover the clinically observed single and multiple-dose plasma concentrations of tipifarnib in healthy subjects and cancer patients under several dosing conditions, such as co-administration with a strong CYP3A4 inhibitor and inducer, an acid-reducing agent (proton pump inhibitor and H2 receptor antagonist), and with a high-fat meal. In addition, the model was able to accurately predict the effect of mild or moderate hepatic impairment on tipifarnib exposure. The appropriately verified model was applied to prospectively simulate the liability of tipifarnib as a victim of CYP3A4 enzyme-based drug-drug interactions (DDIs) with a moderate inhibitor and inducer as well as tipifarnib as a perpetrator of DDIs with sensitive substrates of CYP3A4, CYP2B6, CYP2D6, CYP2C9, and CYP2C19 in healthy subjects and cancer patients. The effect of a high-fat meal, acid-reducing agent, and formulation change at the therapeutic dose was simulated. Finally, the model was used to predict the effect of mild, moderate, or severe hepatic, and renal impairment on tipifarnib PK. This multipronged approach of combining the available clinical data with PBPK modeling-guided dosing recommendations for tipifarnib under several conditions. This example showcases the totality of the data approach to gain a more thorough understanding of clinical pharmacology and biopharmaceutic properties of oncology drugs in development.

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

Preliminary Investigation of a Rapid and Feasible Therapeutic Drug Monitoring Method for the Real-Time Estimation of Blood Pazopanib Concentrations

Pazopanib is a multi-kinase inhibitor used to treat advanced/metastatic renal cell carcinoma and advanced soft tissue tumors; however, side effects such as diarrhea and hypertension have been reported, and dosage adjustment based on drug concentration in the blood is necessary. However, measuring pazopanib concentrations in blood using the existing methods is time-consuming; and current dosage adjustments are made using the results of blood samples taken at the patient's previous hospital visit (approximately a month prior). If the concentration of pazopanib could be measured during the waiting period for a doctor's examination at the hospital (in approximately 30 min), the dosage could be adjusted according to the patient's condition on that day. Therefore, we aimed to develop a method for rapidly measuring blood pazopanib concentrations (in approximately 25 min) using common analytical devices (a tabletop centrifuge and a spectrometer). This method allowed for pazopanib quantification in the therapeutic concentration range (25-50 μg/mL). Additionally, eight popular concomitant medications taken simultaneously with pazopanib did not interfere with the measurements. We used the developed method to measure blood concentration in two patients and obtained similar results to those measured using the previously reported HPLC method. By integrating it with the point of care and sample collection by finger pick, this method can be used for measurements in pharmacies and patients' homes. This method can maximize the therapeutic effects of pazopanib by dose adjustment to control adverse events.

Role of Physiologically Based Biopharmaceutics Modeling (PBBM) in Fed Bioequivalence Study Waivers: Regulatory Outlook, Case Studies and Future Perspectives

Over the past few decades, physiologically based biopharmaceutics modeling (PBBM) has demonstrated its utility in both new drug and generic product development. Applications of PBBM for fed bioequivalence study waivers is an upcoming area. Recently Innovation & Quality (IQ) consortium demonstrated utility of PBBM to avoid repeat food effect studies for new drugs. In the similar lines, the current manuscript aims to discuss role of PBBM in generic fed bioequivalence study waivers. Generic industry practices related to PBBM model development to predict fed bioequivalence was portrayed with special emphasis on fed bio-predictive media. Media that can simulate fed bioequivalence study outcome were discussed from practical perspective. In-depth analysis, collating the data from 36 products was performed to understand predictability of PBBM for fed bioequivalence. Cases where PBBM was successful to predict fed bioequivalence was correlated with BCS class, formulation category and type of food effect. Further, two case studies were presented wherein fed bioequivalence study waiver obtained with PBBM approach. Lastly, future direction in terms of fed bioequivalence study waivers, regulatory perspectives and best practices for PBBM were portrayed. Overall, this article paves a way to utilize PBBM for generic fed bioequivalence study waivers.

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

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

Using Mechanistic Modeling Approaches to Support Bioequivalence Assessments for Oral Products

This report summarizes the proceedings for Day 1 Session 3 of the 2-day public workshop entitled "Best Practices for Utilizing Modeling Approaches to Support Generic Product Development," a jointly sponsored workshop by the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG) in the year 2022. The aims of this workshop were to discuss how to modernize approaches for efficiently demonstrating bioequivalence (BE), to establish their role in modern paradigms of generic drug development, and to explore and develop best practices for the use of modeling and simulation approaches in regulatory submissions and approval. The theme of this session is mechanistic modeling approaches supporting BE assessments for oral drug products. As a summary, with more successful cases of PBPK absorption modeling being developed and shared, the general strategies/frameworks on using PBPK for oral products are being formed; this will help further evolvement of this area. In addition, the early communications between the industry and the agency through appropriate pathways (e.g., pre-abbreviated new drug applications (pre-ANDA) meetings) are encouraged, and this will speed up the successful development and utility of PBPK modeling for oral products.

PBPK Modeling of PAXLOVIDTM: Incorporating Rotamer Conversion Kinetics to Advanced Dissolution and Absorption Model

PAXLOVID™ is a combination medicine of nirmatrelvir tablets co-packaged with ritonavir tablets. Nirmatrelvir is a peptidomimetic inhibitor of SARS-CoV2 main protease (Mpro), developed for the treatment of COVID-19. Ritonavir is co-administered as a pharmacokinetics (PK) enhancer to inhibit CYP3A mediated metabolism increasing exposures of nirmatrelvir. In the solid form, nirmatrelvir exists in a stable single conformational state (ANTI form). However, nirmatrelvir exhibits atropisomerism in solution whereby upon dissolution the ANTI rotational isomer reversibly converts to another conformation state (SYN form). Nirmatrelvir rotamer conversion follows pseudo first order kinetics with a conversion half-life of approximately 15 min in aqueous solutions, which is on a similar time scale of diffusion mediated dissolution from the solid form. In vitro dissolution studies further indicated that rotamer conversion is one of the processes controlling nirmatrelvir dissolution. It was hypothesized that rotamer conversion kinetics would affect oral absorption of nirmatrelvir in vivo. Consequently, a physiologically based pharmacokinetic (PBPK) model for Paxlovid was developed in Simcyp™ using the advanced dissolution, absorption, and metabolism model (ADAM) by incorporating rotamer conversion kinetics to achieve a more mechanistic description of nirmatrelvir oral absorption. The results demonstrate that the established absorption model with rotamer kinetics adequately described observed clinical data from various nirmatrelvir doses, dosage forms, and dosing regimens. The predicted vs. observed AUCinf and Cmax ratios were within 2-fold. The model has been internally used to inform clinical studies and dose recommendations for pediatrics.

Approaches to Account for Colon Absorption in Physiologically Based Biopharmaceutics Modeling of Extended-Release Drug Products

The rate and extent of colon absorption are important determinants of the in vivo performance of extended-release (ER) drug products. The ability to appropriately predict this at different stages of development using mechanistic physiologically based biopharmaceutic modeling (PBBM) is highly desirable. This investigation aimed to evaluate the prediction performance of three different approaches to account for colon absorption in predictions of the in vivo performance of ER drug product variants with different in vitro release profiles. This was done by mechanistic predictions of the absorption and plasma exposure of the ER drug products using GastroPlus and GI-Sim for five drugs with different degrees of colon absorption limitations in humans. Colon absorption was accounted for in the predictions using three different approaches: (1) by an a priori approach using the default colon models, (2) by fitting the colon absorption scaling factors to the observed plasma concentration-time profiles after direct administration to the colon in humans, or (3) from the ER drug product variant with the slowest in vitro release profile. The prediction performance was evaluated based on the percentage prediction error and the average absolute prediction error (AAPE). Two levels of acceptance criteria corresponding to highly accurate (AAPE ≤ 20%) and accurate (AAPE 20-50%) predictions were defined prior to the evaluation. For the a priori approach, the relative bioavailability (Frel), AUC0-t, and Cmax of the ER drug product variants for the low to medium colon absorption limitation risk drugs was accurately predicted with an AAPE range of 11-53 and 8-59% for GastroPlus and GI-Sim, respectively. However, the prediction performance was poor for the high colon absorption limitation risk drugs. Moreover, accounting for the human regional colon absorption data in the models did not improve the prediction performance. In contrast, using the colon absorption scaling factors derived from the slowest ER variant significantly improved the prediction performance regardless of colon absorption limitation, with a majority of the predictions meeting the high accuracy criteria. For the slowest ER approach, the AAPE ranges were 5-24 and 5-32% for GastroPlus and GI-Sim, respectively, excluding the low permeability drug. In conclusion, the a priori PBBM can be used during candidate selection and early product design to predict the in vivo performance of ER drug products for low to medium colon absorption limitation risk drugs with sufficient accuracy. The results also indicate a limited value in performing human regional absorption studies in which the drug is administered to the colon as a bolus to support PBBM development for ER drug products. Instead, by performing an early streamlined relative bioavailability study with the slowest relevant ER in vitro release profile, a highly accurate PBBM suitable for ER predictions for commercial and regulatory applications can be developed, except for permeability-limited drugs.

Analysis of the interplay of physiological response to food intake and drug properties in food-drug interactions

The effect of food on oral drug absorption is determined by the complex interplay among gut physiological factors and drug properties. The currently used dissolution testing and classification systems (biopharmaceutics classification system, BCS or biopharmaceutics drug disposition classification system, BDDCS) do not account for dynamic changes in gastrointestinal physiology caused by food intake. This study aimed to identify key drug properties that influence food effect (FE) using supervised machine learning approaches. The analysis showed that drugs with high logP, dose number, and extraction ratio have a higher probability of positive FE, while drugs with low permeability and high efflux saturation index have a greater likelihood of negative FE. Weakly acidic drugs also showed a greater probability of positive FE, particularly at pKa >4.3. The importance of drug properties in predicting FE was ranked as logP, dose number, extraction ratio, pKa, and permeability. The accuracy of FE prediction using the models was compared with BCS and extended clearance classification system (ECCS). Overall, the likelihood or magnitude of FE depends on physiological changes to food intake such as altered bile acid secretion rate, intestinal metabolism, transport kinetics, and gastric emptying time, which should be considered along with drug properties (e.g., solubility, logP, and ionization) in predicting FE of orally administered drugs.

Food and bile micelle binding of quaternary ammonium compounds

Background and Purpose

Physiologically-based biopharmaceutics modeling (PBBM) has been widely used to predict the oral absorption of drugs. However, the prediction of food effects on oral drug absorption is still challenging, especially for negative food effects. Marked negative food effects have been reported in most cases of quaternary ammonium compounds (QAC). However, the mechanism has remained unclear. The purpose of the present study was to investigate the bile micelle and food binding of QACs as a mechanism of the negative food effect.

Experimental Approach

Trospium (TRS), propantheline (PPT), and ambenonium (AMB) were selected as model QAC drugs. The oral absorption of these QACs has been reported to be reduced by 77% (TRS), > 66% (PPT), and 79% (AMB), when taken with food. The fasted and fed state simulated intestinal fluids (FaSSIF and FeSSIF, containing 3 and 15 mM taurocholic acid, respectively) with or without FDA breakfast homogenate (BFH) were used as the simulated intestinal fluid. The unbound fraction (fu) of the QACs in these media was measured by dynamic dialysis.

Key Results

The fu ratios (FeSSIF/ FaSSIF) were 0.67 (TRS), 0.47 (PPT), and 0.76 (AMB). When BFH was added to FeSSIF, it was reduced to 0.39 (TRS), 0.28 (PPT), and 0.59 (AMB).

Conclusion

These results suggested that bile micelle and food binding play an important role in the negative food effect on the oral absorption of QACs.

Current screening, design, and delivery approaches to address low permeability of chemically synthesized modalities in drug discovery and early clinical development

A drug's permeability across biological membranes is a key property associated with the successful development of an orally absorbed drug candidate. Although a variety of methods are available for predicting and assessing permeability, some are more preferred than others at specific stages of drug discovery and development across the pharmaceutical industry. Permeability measurements may be interpreted differently depending on the chosen method. Herein, we present a refreshed perspective on the screening approaches and philosophy in permeability evaluation, from early drug discovery to early clinical development. Additionally, we review and discuss chemical design and drug delivery technologies that can be leveraged to overcome permeability challenges, which are increasingly being used with emerging modalities.

Exploratory Food Effect Assessment in Patients in Early Clinical Development of Oncology Drugs

Instructions for administration with regard to food are a key aspect of how patients experience oral drugs. Through potential effects on pharmacokinetics, the food condition can influence safety and efficacy, and thereby is one of many dimensions of dose optimization. Regulatory guidance from major health authorities advocates for the early investigation of food effect (FE) in clinical development. In oncology, exploratory FE (eFE) evaluation is often incorporated into the first-in-human (FIH) studies in patients to inform food condition of later clinical studies. However, the design aspects of such exploratory assessments are generally under-reported and barely described, and yet complex, due to uniqueness of FIH study design and drug development process in oncology. Herein, we review literature of eFE assessment study design in oncology in patients, and present the Novartis experience in the design, execution, and impact of eFE in FIH oncology studies from 2014 to 2021. Based on this, we propose a roadmap for eFE assessment in early clinical drug development for oncology drugs in patients, including a framework for common study design options with a focus on study- and patient-level timing for typical scenarios. We also provide a broad spectrum of decision-making factors which should be evaluated to drive the design and implementation of eFE assessment, spanning from clinical development strategy, FIH study design, to compound-specific features.

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.

Parameters to Consider for Successful Medication Use in Older Adults - an AGePOP Review

Older adults are the main users of medicine and due to their multimorbidity are often faced/confronted with a complex medication management. This review article provides a brief overview on aspects of medication management such as maintaining a stock of the required medicine, understanding and following the instructions for use, coping with the primary and secondary packaging as well as preparation prior to use. The main focus however is on the drug intake itself and provides an overview about the current understanding of real life dosing conditions of older adults and geriatric patients. Furthermore, it elaborates the acceptability of dosage forms, in particular solid oral dosage forms as they represent the majority of dosage forms taken by these patient populations. An improved understanding of the needs of older adults and geriatric patients, their acceptability of various dosage forms and the circumstances under which they manage their medications, will make the design of more patient-centric drug products possible.

Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures

Possible drug-food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug-drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament's effect, the withdrawals of various medications, and harmful impacts on the patients' health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug-food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.

Effect of Food and a Proton-Pump Inhibitor on the Absorption of Encorafenib: An In Vivo-In Vitro-In Silico Approach

Encorafenib is a kinase inhibitor indicated for the treatment of patients with BRAF mutant melanoma and BRAF mutant metastatic colorectal cancer. To understand the effect of food and coadministration with a proton-pump inhibitor (PPI), in vitro, in vivo, and in silico data were generated to optimize the clinical dose, evaluate safety, and better understand the oral absorption process under these conditions. Study 1 evaluated the effect of food on the plasma pharmacokinetics, safety, and tolerability after a single oral dose of encorafenib 100 mg. Study 2 evaluated the same end points with coadministration of encorafenib and rabeprazole (PPI perpetrator). The in vitro gastrointestinal TIM-1 model was used to investigate the release of encorafenib and the amount available for absorption under different testing conditions (fasted, fed, and with the use of a PPI). The fasted, fed, and PPI states were predicted for the encorafenib commercial capsule in GastroPlus 9.8. In study 1, both AUC_inf and AUC_last decreased by 4% with the administration of a high-fat meal. The C_max was 36% lower than with fasted conditions. All 3 exposure parameters in study 2 (AUC_inf, AUC_last, and C_max) had mean changes of <10% when encorafenib was coadministered with a PPI. Using the in vitro gastrointestinal simulator TIM-1, the model demonstrated a similar release of drug, as the bioaccessible fraction, in the 3 conditions was equal (≥80%), predicting no PPI or food effect for this drug formulation. The modeling in GastroPlus 9.8 demonstrated complete absorption of encorafenib when formulated as an amorphous solid dispersion. To obtain these results, it was crucial to integrate the amorphous solubility of the drug that shows a 20-fold higher solubility at pH 6.8 compared with crystalline solubility. The increased amorphous solubility is likely the reason no PPI effect was observed compared with fasted state conditions. The prolongation in gastric emptying in the fed state resulted in delayed plasma T_max for encorafenib. No dose adjustment is necessary when encorafenib is administered in the fed state or when coadministered with a PPI. Both the TIM-1 and physiologically based pharmacokinetic model results were consistent with the observed clinical data, suggesting that these will be valuable tools for future work.

Physiologically Based Biopharmaceutics Modeling of regional and colon absorption in humans

Colon absorption is a key determinant for successful development of extended release and colon targeted drug products. This is the first systematic evaluation of the ability to predict in vivo regional differences in absorption and the extent of colon absorption in humans using mechanistic physiologically based biopharmaceutics modeling (PBBM). A new dataset, consisting of 19 drugs with a wide range of biopharmaceutics properties and extent of colon absorption in humans, was established. Mechanistic predictions of the extent of absorption and plasma exposure after oral, or jejunal and direct colon administration were performed in GastroPlus and GI-Sim using an a priori approach. Two new colon models developed in GI-Sim, were also evaluated to assess if the prediction performance could be improved. Both GastroPlus and GI-Sim met the pre-defined criteria for accurate predictions of regional and colon absorption for high permeability drugs irrespective of formulation type, while the prediction performance was poor for low permeability drugs. For solutions, the two new GI-Sim colon models improved the colon absorption prediction performance for the low permeability drugs while maintaining the accurate prediction performance for the high permeability drugs. In contrast, the prediction performance decreased for non-solutions using the two new colon models. In conclusion, PBBM can be used with sufficient accuracy to predict regional and colon absorption in humans for high permeability drugs in candidate selection as well as early design and development of extended release or colon targeted drug products. The prediction performance of the current models needs to be improved to allow high accuracy predictions for commercial drug product applications including highly accurate predictions of the entire plasma concentration-time profiles as well as for low permeability drugs.

Predicting Food Effects on Oral Extended-Release Drug Products: A Retrospective Evaluation

Theoretically, the risk of food effects for extended-release (ER) products compared to IR products may be less because: (1) postprandial physiological changes are usually transient and last for 2-3 h only; and (2) the percentage of drug release from an ER product within the first 2-3 h post dose is usually small under both fasted and fed states. The major postprandial physiological changes that can affect oral absorption of ER drugs are delayed gastric emptying and prolonged intestinal transit. Oral absorption of ER drugs under fasted state mainly occurs in large intestine (colon and rectum) while the absorption of ER drugs under fed state occurs in both small and large intestines. We hypothesized that food effects for ER products are mainly caused by intestinal region-dependent absorption and food intake is more likely to increase rather than decrease the exposure of ER products due to a longer transit time and improved absorption in small intestine. For drugs with good absorption from large intestine, food effects on the area under the curve (AUC) of ER products are usually not expected. Our survey of oral drugs approved by the US FDA between 1998-2021 identified 136 oral ER drug products. Among the 136 ER drug products, 31, 6 and 99 products exhibited increased, decreased, and unchanged AUC under fed conditions, respectively. In general, when an ER product exhibits a fasted bioavailability (BA) relative to its corresponding immediate-release (IR) product between 80-125%, regardless the solubility or permeability of drug substances, substantial food effects on the AUC of ER product are generally not expected. If the fasted relative BA data are not available, a high in vitro permeability (i.e., Caco-2 or MDCK cell permeability comparable or higher than that of metoprolol) may inform no food effect on the AUC of an ER product of high-solubility (BCS class I and III) drug.

Assessment of food effects during clinical development

Food-drug interactions frequently hamper oral drug development due to various physicochemical, physiological and formulation-dependent mechanisms. This has stimulated the development of a range of promising biopharmaceutical assessment tools which, however, lack standardized settings and protocols. Hence, this manuscript aims to provide an overview of the general approach and the methodology used in food effect assessment and prediction. For in vitro dissolution-based predictions, the expected food effect mechanism should be carefully considered when selecting the level of complexity of the model, together with its drawbacks and advantages. Typically, in vitro dissolution profiles are then incorporated into physiologically based pharmacokinetic models, which can estimate the impact of food-drug interactions on bioavailability within 2-fold prediction error, at least. Positive food effects related to drug solubilization in the GI tract are easier to predict than negative food effects. Preclinical animal models also provide a good level of food effect prediction, with beagle dogs remaining the gold standard. When solubility-related food-drug interactions have large clinical impact, advanced formulation approaches can be used to improve fasted state pharmacokinetics, hence decreasing the fasted/fed difference in oral bioavailability. Finally, the knowledge from all studies should be combined to secure regulatory approval of the labelling instructions.

Leveraging Physiologically Based Modelling to Provide Insights on the Absorption of Paliperidone Extended-Release Formulation under Fed and Fasting Conditions

Paliperidone was approved by the US FDA in 2006 as an extended-release (ER) tablet (Invega^®) for the once-daily treatment of schizophrenia. This osmotic-controlled release oral delivery system (OROS) offers advantages, such as the prevention of plasma concentration fluctuation and reduced dosing frequency. The administration of the ER after a high-fat/high-calorie meal leads to increased maximum plasma concentration and area under the curve values by 60% and 54%, respectively. Food has various effects on gastrointestinal (GI) physiology, including changed transit times, changed volumes, altered pH in different GI compartments, secretion of bile salts, and increased hepatic blood flow. This may affect solubility, the dissolution rate, absorption, and the pharmacokinetics. The aim of this study was to apply physiologically based absorption modeling (PBAM) to provide insights on paliperidone ER absorption under fed and fasting conditions. The PBAM adequately predicted absorption from the OROS formulation under both conditions. Absorption primarily occurs in the ascending colon and caecum. After a high-fat/high-calorie meal, absorption is increased through the jejunum, ileum, and colon due to either increased solubilization or the better efficiency of the OROS technology. PBAM-guided approaches can improve the understanding of branded drugs and thereby aid in guiding the development of generic formulations or formulation alternatives.

Integrating In Vitro Biopharmaceutics into Physiologically Based Biopharmaceutic Model (PBBM) to Predict Food Effect of BCS IV Zwitterionic Drug (GSK3640254)

A strategy followed to integrate in vitro solubility and permeability data into a PBBM model to predict the food effect of a BCS IV zwitterionic drug (GSK3640254) observed in clinical studies is described. The PBBM model was developed, qualified and verified using clinical data of an immediate release (IR)-tablet (10-320 mg) obtained in healthy volunteers under fasted and fed conditions. The solubility of GSK3640254 was a function of its ionization state, the media composition and pH, whereas its permeability determined using MDCK cell lines was enhanced by the presence of mixed micelles. In vitro data alongside PBBM modelling suggested that the positive food effect observed in the clinical studies was attributed to micelle-mediated enhanced solubility and permeability. The biorelevant media containing oleic acid and cholesterol in fasted and fed levels enabled the model to appropriately capture the magnitude of the food effect. Thus, by using Simcyp^® v20 software, the PBBM model accurately predicted the results of the food effect and predicted data were within a two-fold error with 70% being within 1.25-fold. The developed model strategy can be effectively adopted to increase the confidence of using PBBM models to predict the food effect of BCS class IV drugs.

Current State and Challenges of Physiologically Based Biopharmaceutics Modeling (PBBM) in Oral Drug Product Development

Physiologically based biopharmaceutics modeling (PBBM) emphasizes the integration of physicochemical properties of drug substance and formulation characteristics with system physiological parameters to predict the absorption and pharmacokinetics (PK) of a drug product. PBBM has been successfully utilized in drug development from discovery to postapproval stages and covers a variety of applications. The use of PBBM facilitates drug development and can reduce the number of preclinical and clinical studies. In this review, we summarized the major applications of PBBM, which are classified into six categories: formulation selection and development, biopredictive dissolution method development, biopharmaceutics risk assessment, clinically relevant specification settings, food effect evaluation and pH-dependent drug-drug-interaction risk assessment. The current state of PBBM applications is illustrated with examples from published studies for each category of application. Despite the variety of PBBM applications, there are still many hurdles limiting the use of PBBM in drug development, that are associated with the complexity of gastrointestinal and human physiology, the knowledge gap between the in vitro and the in vivo behavior of drug products, the limitations of model interfaces, and the lack of agreed model validation criteria, among other issues. The challenges and essential considerations related to the use of PBBM are discussed in a question-based format along with the scientific thinking on future research directions. We hope this review can foster open discussions between the pharmaceutical industry and regulatory agencies and encourage collaborative research to fill the gaps, with the ultimate goal to maximize the applications of PBBM in oral drug product development.

Integrating Forward and Reverse Translation in PBPK Modeling to Predict Food Effect on Oral Absorption of Weakly Basic Drugs

INTRODUCTION

Ketoconazole and posaconazole are two weakly basic broad-spectrum antifungals classified as Biopharmaceutics Classification System class II drugs, indicating that they are highly permeable, but exhibit poor solubility. As a result, oral bioavailability and clinical efficacy can be impacted by the formulation performance in the gastrointestinal system. In this work, we have leveraged in vitro biopharmaceutics and clinical data available in the literature to build physiologically based pharmacokinetic (PBPK) models for ketoconazole and posaconazole, to determine the suitability of forward in vitro-in vivo translation for characterization of in vivo drug precipitation, and to predict food effect.

METHODS

A stepwise modeling approach was utilized to derive key parameters related to absorption, such as drug solubility, dissolution, and precipitation kinetics from in vitro data. These parameters were then integrated into PBPK models for the simulation of ketoconazole and posaconazole plasma concentrations in the fasted and fed states.

RESULTS

Forward in vitro-in vivo translation of intestinal precipitation kinetics for both model drugs resulted in poor predictions of PK profiles. Therefore, a reverse translation approach was applied, based on limited fitting of precipitation-related parameters to clinical data. Subsequent simulations for ketoconazole and posaconazole demonstrated that fasted and fed state PK profiles for both drugs were adequately recapitulated.

CONCLUSION

The two examples presented in this paper show how middle-out modeling approaches can be used to predict the magnitude and direction of food effects provided the model is verified on fasted state PK data.

Physiologically Based Biopharmaceutics Modeling of Food Effect for Basmisanil: A Retrospective Case Study of the Utility for Formulation Bridging

Basmisanil, is a lipophilic drug substance, exhibiting poor solubility and good permeability (BCS class 2). A validated physiologically based biopharmaceutics model (PBBM) has been previously described for tablets dosed in the fed state. The PBBM captured the less than proportional increases in exposure at higher doses well and indicated that absorption was dissolution rate-limited below 200 mg while solubility was limiting for higher doses. In this study, a model for dosing in the fasted state is described and is verified for simulation of the food effect where exposures were ~1.5 fold higher when a 660 mg tablet was given with food. The model is then applied to simulate the food effect for a granules formulation given at a lower dose (120 mg). The food effect at the lower dose was reasonably simulated with a ratio of simulated/observed food effect of 1.35 for Cmax and 0.83 for AUC. Sensitivity analysis was carried out for uncertain model parameters to confirm that the model could predict the magnitude of the positive food effect with moderate to high confidence. This study suggests that a verified PBBM can provide a useful alternative to a repeat food effect study when formulation changes are minor. However, there is need for further evaluation of the approach and a definition of what formulation changes are minor in this context. In addition, this work highlights some uncertainties in the handling of solubility in PBBM, in particular around temperature dependency of solubility and the parameterization of bile salt solubilization using measurements in biorelevant media.

Regulatory utility of physiologically based pharmacokinetic modeling for assessing food impact in bioequivalence studies: A workshop summary report

This workshop report summarizes the presentations and panel discussion related to the use of physiologically based pharmacokinetic (PBPK) modeling approaches for food effect assessment, collected from Session 2 of Day 2 of the workshop titled "Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches." The US Food and Drug Administration in collaboration with the Center for Research on Complex Generics organized this workshop where this particular session titled "Oral PBPK for Evaluating the Impact of Food on BE" presented successful cases of PBPK modeling approaches for food effect assessment. Recently, PBPK modeling has started to gain popularity among academia, industries, and regulatory agencies for its potential utility during bioavailability (BA) and/or bioequivalence (BE) studies of new and generic drug products to assess the impact of food on BA/BE. Considering the promises of PBPK modeling in generic drug development, the aim of this workshop session was to facilitate knowledge sharing among academia, industries, and regulatory agencies to understand the knowledge gap and guide the path forward. This report collects and summarizes the information presented and discussed during this session to disseminate the information into a broader audience for further advancement in this area.

Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling

This study aimed to apply a physiologically based pharmacokinetic (PBPK) model to predict optimal dosing regimens of pazopanib (PAZ) for safe and effective administration when co-administered with CYP3A4 inhibitors, acid-reducing agents, food, and administered in patients with hepatic impairment. Here, we have successfully developed the population PBPK model and the predicted PK variables by this model matched well with the clinically observed data. Most ratios of prediction to observation were between 0.5 and 2.0. Suitable dosage modifications of PAZ have been identified using the PBPK simulations in various situations, i.e., 200 mg once daily (OD) or 100 mg twice daily (BID) when co-administered with the two CYP3A4 inhibitors, 200 mg BID when simultaneously administered with food or 800 mg OD when avoiding food uptake simultaneously. Additionally, the PBPK model also suggested that dosing does not need to be adjusted when co-administered with esomeprazole and administration in patients with wild hepatic impairment. Furthermore, the PBPK model also suggested that PAZ is not recommended to be administered in patients with severe hepatic impairment. In summary, the present PBPK model can determine the optimal dosing adjustment recommendations in multiple clinical uses, which cannot be achieved by only focusing on AUC linear change of PK.

Mechanisms and Extent of Enhanced Passive Permeation by Colloidal Drug Particles

Formulations containing nanosized drug particles such as nanocrystals and nanosized amorphous drug aggregates recently came into light as promising strategies to improve the bioavailability of poorly soluble drugs. However, the increased solubility due to the reduction in particle size cannot adequately explain the enhanced bioavailability. In this study, the mechanisms and extent of enhanced passive permeation by drug particles were investigated using atazanavir, lopinavir, and clotrimazole as model drugs. Franz diffusion cells with lipid-infused membranes were utilized to evaluate transmembrane flux. The impact of stirring rate, receiver buffer condition, and particle size was investigated, and mass transport analyses were conducted to calculate transmembrane flux. Flux enhancement by particles was found to be dependent on particle size as well as the partitioning behavior of the drug between the receiver solution and the membrane, which is determined by both the drug and buffer used. A flux plateau was observed at high particle concentrations above amorphous solubility, confirming that mass transfer of amorphous drug particles from the aqueous solution to the membrane occurs only through the molecularly dissolved drug. Mass transport models were used to calculate flux enhancement by particles for various drugs at different conditions. Good agreements were obtained between experimental and predicted values. These results should contribute to improved bioavailability prediction of nanosized drug particles and better design of formulations containing colloidal drug particles.

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

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

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

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

Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery

Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.

Practical and operational considerations related to paediatric oral drug formulation: an industry survey

For over 15 years, US and EU regulations ensure that medicines developed for children are explicitly authorised for such use with age-appropriate forms and formulations, implying dedicated research. To shed light on how these regulations have been adopted by pharmaceutical companies and how various aspects of paediatric oral drug formulation development are currently handled, an exploratory survey was conducted. Topics included: general company policy, regulatory aspects, dosage form selection, in-vitro, in-silico and (non-)clinical in-vivo methods, and food effects assessment. The survey results clearly underline the positive impact of the paediatric regulations and their overall uptake across the pharmaceutical industry. Even though significant improvements have been made in paediatric product development, major challenges remain. In this respect, dosage form selection faces a discrepancy between the youngest age groups (liquid products preference) and older subpopulations (adult formulation preference). Additionally, concerted research is needed in the development and validation of in-vitro tools and physiology based pharmacokinetic models tailored to the paediatric population, and in estimating the effect of non-standard and paediatric relevant foods. The current momentum in paediatric drug development and research should allow for an evolution in standardised methodology and guidance to develop paediatric formulations, which would benefit pharmaceutical industry and regulators.

Development of In Vitro Dissolution Testing Methods to Simulate Fed Conditions for Immediate Release Solid Oral Dosage Forms

In vitro dissolution testing is widely used to mimic and predict in vivo performance of oral drug products in the gastrointestinal (GI) tract. This literature review assesses the current in vitro dissolution methodologies being employed to simulate and predict in vivo drug dissolution under fasted and fed conditions, with emphasis on immediate release (IR) solid oral dosage forms. Notable human GI physiological conditions under fasted and fed states have been reviewed and summarized. Literature results showed that dissolution media, mechanical forces, and transit times are key dissolution test parameters for simulating specific postprandial conditions. A number of biorelevant systems, including the fed stomach model (FSM), GastroDuo device, dynamic gastric model (DGM), simulated gastrointestinal tract models (TIM), and the human gastric simulator (HGS), have been developed to mimic the postprandial state of the stomach. While these models have assisted in expanding physiological relevance of in vitro dissolution tests, in general, these models lack the ability to fully replicate physiological conditions/processes. Furthermore, the translatability of in vitro data to an in vivo system remains challenging. Additionally, physiologically based pharmacokinetic (PBPK) modeling has been employed to evaluate the effect of food on drug bioavailability and bioequivalence. Here, we assess the current status of in vitro dissolution methodologies and absorption PBPK modeling approaches to identify knowledge gaps and facilitate further development of in vitro dissolution methods that factor in fasted and fed states. Prediction of in vivo drug performance under fasted and fed conditions via in vitro dissolution testing and modeling may potentially help efforts in harmonizing global regulatory recommendations regarding in vivo fasted and fed bioequivalence studies for solid oral IR products.

Characterizing the Physicochemical Properties of Two Weakly Basic Drugs and the Precipitates Obtained from Biorelevant Media

Generally, some weakly basic insoluble drugs will undergo precipitate and redissolution after emptying from the stomach to the small intestinal, resulting in the limited ability to predict the absorption characteristics of compounds in advance. Absorption is determined by the solubility and permeability of compounds, which are related to physicochemical properties, while knowledge about the absorption of redissolved precipitate is poorly documented. Considering that biorelevant media have been widely used to simulate gastrointestinal fluids, sufficient precipitates can be obtained in biorelevant media in vitro. Herein, the purpose of this manuscript is to evaluate the physicochemical properties of precipitates obtained from biorelevant media and active pharmaceutical ingredients (API), and then to explore the potential absorption difference between API and precipitates. Precipitates can be formed by the interaction between compounds and intestinal fluid contents, leading to changes in the crystal structure, melting point, and melting process. However, the newly formed crystals have some advantageous properties compared with the API, such as the improved dissolved rate and the increased intrinsic dissolution rate. Additionally, the permeability of some precipitates obtained from biorelevant media was different from API. Meanwhile, the permeability of rivaroxaban and Drug-A was decreased by 1.92-fold and 3.53-fold, respectively, when the experiments were performed in a biorelevant medium instead of a traditional medium. Therefore, the absorption of precipitate may differ from that of API, and the permeability assay in traditional medium may be overestimated. Based on the research results, it is crucial to understand the physicochemical properties of precipitates and API, which can be used as the departure point to improve the prediction performance of absorption.

Exploring the Relationship of Drug BCS Classification, Food Effect, and Gastric pH-Dependent Drug Interactions

Food effect (FE) and gastric pH-dependent drug-drug interactions (DDIs) are both absorption-related. Here, we evaluated if Biopharmaceutics Classification System (BCS) classes may be correlated with FE or pH-dependent DDIs. Trends in FE data were investigated for 170 drugs with clinical FE studies from the literature and new drugs approved from 2013 to 2019 by US Food and Drug Administration. A subset of 38 drugs was also evaluated to determine whether FE results can inform the need for a gastric pH-dependent DDI study. The results of FE studies were defined as no effect (AUC ratio 0.80-1.25), increased exposure (AUC ratio ≥1.25), or decreased exposure (AUC ratio ≤0.8). Drugs with significantly increased exposure FE (AUC ratio ≥2.0; N=14) were BCS Class 2 or 4, while drugs with significantly decreased exposure FE (AUC ratio ≤0.5; N=2) were BCS Class 1/3 or 3. The lack of FE was aligned with the lack of a pH-dependent DDI for all 7 BCS Class 1 or 3 drugs as expected. For the 13 BCS Class 2 or 4 weak base drugs with an increased exposure FE, 6 had a pH-dependent DDI (AUC ratio ≤0.8). Among the 16 BCS Class 2 or 4 weak base drugs with no FE, 6 had a pH-dependent DDI (AUC ratio ≤0.8). FE appears to have limited correlation with BCS classes except for BCS Class 1 drugs, confirming that multiple physiological mechanisms can impact FE. Lack of FE does not indicate absence of pH-dependent DDI for BCS Class 2 or 4 drugs. Graphical Abstract.

Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective

This collection of contributions from the European Network on Understanding Gastrointestinal Absorption-related Processes (UNGAP) community assembly aims to provide information on some of the current and newer methods employed to study the behaviour of medicines. It is the product of interactions in the immediate pre-Covid period when UNGAP members were able to meet and set up workshops and to discuss progress across the disciplines. UNGAP activities are divided into work packages that cover special treatment populations, absorption processes in different regions of the gut, the development of advanced formulations and the integration of food and pharmaceutical scientists in the food-drug interface. This involves both new and established technical approaches in which we have attempted to define best practice and highlight areas where further research is needed. Over the last months we have been able to reflect on some of the key innovative approaches which we were tasked with mapping, including theoretical, in silico, in vitro, in vivo and ex vivo, preclinical and clinical approaches. This is the product of some of us in a snapshot of where UNGAP has travelled and what aspects of innovative technologies are important. It is not a comprehensive review of all methods used in research to study drug dissolution and absorption, but provides an ample panorama of current and advanced methods generally and potentially useful in this area. This collection starts from a consideration of advances in a priori approaches: an understanding of the molecular properties of the compound to predict biological characteristics relevant to absorption. The next four sections discuss a major activity in the UNGAP initiative, the pursuit of more representative conditions to study lumenal dissolution of drug formulations developed independently by academic teams. They are important because they illustrate examples of in vitro simulation systems that have begun to provide a useful understanding of formulation behaviour in the upper GI tract for industry. The Leuven team highlights the importance of the physiology of the digestive tract, as they describe the relevance of gastric and intestinal fluids on the behaviour of drugs along the tract. This provides the introduction to microdosing as an early tool to study drug disposition. Microdosing in oncology is starting to use gamma-emitting tracers, which provides a link through SPECT to the next section on nuclear medicine. The last two papers link the modelling approaches used by the pharmaceutical industry, in silico to Pop-PK linking to Darwich and Aarons, who provide discussion on pharmacometric modelling, completing the loop of molecule to man.

Development and application of a physiologically based pharmacokinetic model for entrectinib in rats and scale-up to humans: Route-dependent gut wall metabolism

Entrectinib (Rozlytrek®) is an oral antineoplastic agent approved by the U.S. Food and Drug Administration in 2019 for the treatment of c-ros oncogene 1 (ROS1)-positive non-small cell lung cancer and neurotrophic tyrosine receptor kinase (NTRK) fusion-positive solid tumors. Although there have been a few studies on the pharmacokinetics of entrectinib, the relative contributions of several kinetic factors determining the oral bioavailability and systemic exposure of entrectinib are still worthy of investigation. Experimental data on the intestinal absorption and disposition of entrectinib in rats were acquired from studies on in vitro protein binding/tissue S9 metabolism, in situ intestinal perfusion, and in vivo dose-escalation/hepatic extraction. Using these datasets, an in-house whole-body physiologically based pharmacokinetic (PBPK) model incorporating the Q_Gut model concepts and segregated blood flow in the gut was constructed and optimized with respect to drug-specific parameters. The established rat PBPK model was further extrapolated to humans through relevant physiological scale-up and parameter optimization processes. The optimized rat and human PBPK models adequately captured the impact of route-dependent gut metabolism on the systemic exposure to entrectinib and closely mirrored various preclinical and clinical observations. Our proposed PBPK model could be useful in optimizing dosage regimens and predicting drug interaction potential in various clinical conditions, after partial modification and validation.

Comparison of Predictions by BCS, rDCS and Machine Learning for the Effect of Food on Oral Drug Absorption Based on Features Calculated In silico

In this study, observed food effects of 473 drugs were categorized into positive, negative, or no effects and compared with the predictions made by machine learning (ML), the Biopharmaceutics Classification System (BCS) and refined Developability Classification System (rDCS). All methods used primarily in silico estimates for prediction, and for ML, four algorithms were evaluated using nested cross-validation to select important information from 371 features calculated based on the chemical structure. Approximately 18 features, including estimated solubility in biorelevant media, were selected as important, and the random forest classifier was the best among four algorithms with 36.6% error rate (ER) and 10.8% opposite prediction rate (OPR). The prediction by rDCS utilizing solubility in a biorelevant medium was somewhat inferior, but not by much; 41.0% ER and 11.4% OPR. Compared with these two methods, the prediction by BCS was inferior; 54.5% ER and 21.4% OPR. ER was improved modestly by using measured features instead of in silico estimates when BCS was applied to a subset of 151 drugs (46.4% from 55.0%). ML and rDCS predicted the food effects of the same subset using in silico estimates with ERs of 37.7% and 42.4%, respectively, suggesting that the predictions by ML and rDCS using in silico features are similar or more accurate than those by BCS using measured features. These results suggest that ML was useful in revealing essential features from complex information and, together with rDCS, is effective in predicting food effects during drug development, including early drug discovery.

Artemisinin Cocrystals for Bioavailability Enhancement. Part 2: In Vivo Bioavailability and Physiologically Based Pharmacokinetic Modeling

We report the evaluation and prediction of the pharmacokinetic (PK) performance of artemisinin (ART) cocrystal formulations, that is, 1:1 artemisinin/orcinol (ART-ORC) and 2:1 artemisinin/resorcinol (ART²-RES), using in vivo murine animal and physiologically based pharmacokinetic (PBPK) models. The efficacy of the ART cocrystal formulations along with the parent drug ART was tested in mice infected with Plasmodium berghei. When given at the same dose, the ART cocrystal formulation showed a significant reduction in parasitaemia at day 4 after infection compared to ART alone. PK parameters including C_max (maximum plasma concentration), T_max (time to C_max), and AUC (area under the curve) were obtained by determining drug concentrations in the plasma using liquid chromatography-high-resolution mass spectrometry (LC-HRMS), showing enhanced ART levels after dosage with the cocrystal formulations. The dose-response tests revealed that a significantly lower dose of the ART cocrystals in the formulation was required to achieve a similar therapeutic effect as ART alone. A PBPK model was developed using a PBPK mouse simulator to accurately predict the in vivo behavior of the cocrystal formulations by combining in vitro dissolution profiles with the properties of the parent drug ART. The study illustrated that information from classical in vitro and in vivo experimental investigations of the parent drug of ART formulations can be coupled with PBPK modeling to predict the PK parameters of an ART cocrystal formulation in an efficient manner. Therefore, the proposed modeling strategy could be used to establish in vitro and in vivo correlations for different cocrystals intended to improve dissolution properties and to support clinical candidate selection, contributing to the assessment of cocrystal developability and formulation development.

Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks

Despite countless advances in recent decades across various in vitro, in vivo and in silico tools, anticipation of whether a drug will show a human food effect (FE) remains challenging. One means to predict potential FE involves probing any dependence between FE and drug properties. Accordingly, this study explored the potential for two machine learning (ML) algorithms to predict likely FE. Using a collated database of drugs licensed from 2016-2020, drugs were classified into three groups; positive, negative or no FE. Greater than 250 drug properties were predicted for each drug which were used to train predictive models using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. When compared, ANN outperformed SVM for FE classification upon training (82%, 72%) and testing (72%, 69%). Both models demonstrated higher FE prediction accuracy than the Biopharmaceutics Classification System (BCS) (46%). This exploratory work provided new insights into the connection between FE and drug properties as the Octanol Water Partition Coefficient (S+logP), Number of Hydrogen Bond Donors (HBD), Topological Polar Surface Area (T_PSA) and Dose (mg) were all significant for prediction. Overall, this study demonstrated the utility of ML to facilitate early anticipation of likely FE in pre-clinical development using four well-known drug properties.

Current challenges and future perspectives in oral absorption research: An opinion of the UNGAP network

Although oral drug delivery is the preferred administration route and has been used for centuries, modern drug discovery and development pipelines challenge conventional formulation approaches and highlight the insufficient mechanistic understanding of processes critical to oral drug absorption. This review presents the opinion of UNGAP scientists on four key themes across the oral absorption landscape: (1) specific patient populations, (2) regional differences in the gastrointestinal tract, (3) advanced formulations and (4) food-drug interactions. The differences of oral absorption in pediatric and geriatric populations, the specific issues in colonic absorption, the formulation approaches for poorly water-soluble (small molecules) and poorly permeable (peptides, RNA etc.) drugs, as well as the vast realm of food effects, are some of the topics discussed in detail. The identified controversies and gaps in the current understanding of gastrointestinal absorption-related processes are used to create a roadmap for the future of oral drug absorption research.

Lost in modelling and simulation?

Over the past few decades, physiologically-based pharmacokinetic modelling (PBPK) has been anticipated to be a powerful tool to improve the productivity of drug discovery and development. However, recently, multiple systematic evaluation studies independently suggested that the predictive power of current oral absorption (OA) PBPK models needs significant improvement. There is some disagreement between the industry and regulators about the credibility of OA PBPK modelling. Recently, the editorial board of AMDET&DMPK has announced the policy for the articles related to PBPK modelling (Modelling and simulation ethics). In this feature article, the background of this policy is explained: (1) Requirements for scientific writing of PBPK modelling, (2) Scientific literacy for PBPK modelling, and (3) Middle-out approaches. PBPK models are a useful tool if used correctly. This article will hopefully help advance the science of OA PBPK models.