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To date, mechanistic modeling of oral drug absorption has been achieved via the use of physiologically based pharmacokinetic (PBPK) modeling, and more specifically, physiologically based biopharmaceutics model (PBBM). The concept of finite absorption time (FAT) has been developed recently and the application of the relevant physiologically based finite time pharmacokinetic (PBFTPK) models to experimental data provides explicit evidence that drug absorption terminates at a specific time point. In this manuscript, we explored how PBBM and PBFTPK models compare when applied to the same dataset. A set of six compounds with clinical data from immediate-release formulation were selected. Both models resulted in absorption time estimates within the small intestinal transit time, with PBFTPK models generally providing shorter time estimates. A clear relationship between the absorption rate and the product of permeability and luminal concentration was observed, in concurrence with the fundamental assumptions of PBFTPK models. We propose that future research on the synergy between the two modeling approaches can lead to both improvements in the initial parameterization of PBPK/PBBM models but to also expand mechanistic oral absorption concepts to more traditional pharmacometrics applications.

It is now recognized that a number of excipients previously considered to be "inert" have the capacity to alter drug oral bioavailability through a range of in vivo effects. The various mechanisms through which an excipient can affect in vivo gastrointestinal physiology and drug absorption characteristics were explored in "A Critical Overview of The Biological Effects of Excipients (Part I): Impact on Gastrointestinal Absorption." The next critical issue that needs to be discussed is how these biological effects are evaluated. Therefore, in Part 2 of this critical overview, the in vitro, in vivo, and in silico methods for evaluating excipient effects are considered. Examples are provided to illustrate how such studies employing these various procedures have been used to promote formulation understanding and optimization. Finally, a discussion of how the Center for Drug Evaluation and Research applies these tools to support biowaivers is provided.

Assessment of bioperformance to inform formulation selection and development decisions is an important aspect of drug development. There is high demand in the pharmaceutical industry to develop an efficient and streamlined approach for better understanding and predicting drug product performance to support acceleration of clinical timelines. This manuscript presents an effort from the IQ Formulation Bioperformance Prediction Working Group composed of members from 12 pharmaceutical companies under the IQ Consortium to develop a database around the topic of formulation bioperformance prediction and report findings from the database analysis. Six case studies described in the manuscript demonstrate how bioperformance models were used to predict in vivo performance and to provide guidance addressing questions encountered during oral solid dosage form development. The case studies also described findings of a correlation between in vitro dissolution and in vivo performance and how dissolution data can be incorporated into physiologically based biopharmaceutical modeling. Finally, a workflow for how in vitro dissolution data can be utilized to predict clinical bioperformance of oral solid dosage forms is proposed.

Food can alter drug absorption and impact safety and efficacy. Besides conducting clinical studies, in vitro approaches such as biorelevant solubility and dissolution testing and in vivo dog studies are typical approaches to estimate a drug's food effect. The use of physiologically based pharmacokinetic models has gained importance and is nowadays a standard tool for food effect predictions at preclinical and clinical stages in the pharmaceutical industry. This manuscript is part of a broader publication from the IQ Consortium's food effect physiologically based pharmacokinetic model (PBPK) modeling working group and complements previous publications by focusing on cases where the food effect was predicted with low confidence. Pazopanib-HCl, trospium-Cl, and ziprasidone-HCl served as model compounds to provide insights into why several food effect predictions failed in the first instance. Furthermore, the manuscript depicts approaches whereby PBPK-based food effect predictions may be improved. These improvements should focus on the PBPK model functionality, especially better reflecting fasted- and fed-state gastric solubility, gastric re-acidification, and complex mechanisms related to gastric emptying of drugs. For improvement of in vitro methodologies, the focus should be on the development of more predictive solubility, supersaturation, and precipitation assays. With regards to the general PBPK modeling methodology, modelers should account for the full solubility profile when modeling ionizable compounds, including common ion effects, and apply a straightforward strategy to account for drug precipitation.

Decision-making in drug development benefits from an integrated systems approach, where the stakeholders identify and address the critical questions for the system through carefully designed and performed studies. Biopharmaceutics Risk Assessment Roadmap (BioRAM) is such a systems approach for application of systems thinking to patient focused and timely decision-making, suitable for all stages of drug discovery and development. We described the BioRAM therapy-driven drug delivery framework, strategic roadmap, and integrated risk assessment instrument (BioRAM Scoring Grid) in previous publications (J Pharm Sci 103:3377–97, 2014; J Pharm Sci 105:3243–55, 2016). Integration of systems thinking with pharmaceutical development, manufacturing, and clinical sciences and health care is unique to BioRAM where the developed strategy identifies the system and enables risk characterization and balancing for the entire system. Successful decision-making process in BioRAM starts with the Blueprint (BP) meetings. Through shared understanding of the system, the program strategy is developed and captured in the program BP. Here, we provide three semi-hypothetical examples for illustrating risk-based decision-making in high and moderate risk settings. In the high-risk setting, which is a rare disease area, two completely alternate development approaches are considered (gene therapy and small molecule). The two moderate-risk examples represent varied knowledge levels and drivers for the programs. In one moderate-risk example, knowledge leveraging opportunities are drawn from the manufacturing knowledge and clinical performance of a similar drug substance. In the other example, knowledge on acute tolerance patterns for a similar mechanistic pathway is utilized for identifying markers to inform the drug release profile from the dosage form with the necessary “flexibility” for dosing. All examples illustrate implementation of the BioRAM strategy for leveraging knowledge and decision-making to optimize the clinical performance of drug products for patient benefit.

The effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro, and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low ( 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to changes in the gastrointestinal luminal fluids or physiology, including fluid volume, motility, pH, micellar entrapment, and bile salts. Considering these findings, it is recommended that appropriately verified mechanistic PBPK modeling can be leveraged with high to moderate confidence as a key approach to predicting potential food effect, especially related to mechanisms highlighted here.

The effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro, and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low (> 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to changes in the gastrointestinal luminal fluids or physiology, including fluid volume, motility, pH, micellar entrapment, and bile salts. Considering these findings, it is recommended that appropriately verified mechanistic PBPK modeling can be leveraged with high to moderate confidence as a key approach to predicting potential food effect, especially related to mechanisms highlighted here. Electronic supplementary material The online version of this article (10.1208/s12248-020-00508-2) contains supplementary material, which is available to authorized users.

Despite many documented differences in gut physiology compared to humans, the beagle dog has been successfully used as a preclinical model for assessing the relative bioavailability of dosage forms during formulation development. However, differences in pH and bile salt concentration and micellar structure between dog and human intestinal fluids may influence the solubility and dissolution behavior of especially BCS II/IV compounds. Recently, a canine fasted simulated intestinal fluid (FaSSIFc) mimicking the composition in the lumen of the beagle dog under the fasted state has been proposed. In this manuscript, we present the utilization of FaSSIFc to compare solubility of several preclinical candidates against human FaSSIF. While solubility of free bases and neutral compounds was easily predicted by the relative amounts of sodium taurocholate in the fluids, free acids were shown to be much more soluble in FaSSIFc owing to both the solubility at higher pH as well as the increased bile salt concentration. For one of the model compounds, we demonstrate that the high solubility necessitates the need for a formulation comparison at a relatively higher dose in the dog to mimic the outcome of a human relative bioavailability study. Finally, we show how using the solubility value in FaSSIFc for the same compound results in better predictability of the plasma concentration profiles in dogs from a physiologically based absorption model. The collective data indicate that caution and more detailed measurements are required if the dog is used as the preclinical model for the development of formulations of weak acids.

This publication summarizes the proceedings of day 2 of a 3-day workshop on "Dissolution and Translational Modeling Strategies Enabling Patient-Centric Product Development." Patient-centric drug product development from a drug product quality perspective necessitates the establishment of clinically relevant drug product specifications via an in vitro-in vivo link. Modeling and simulation offer a path to establish this link; in this regard, physiologically based modeling has been implemented successfully to support regulatory decision-making and drug product labeling. In this manuscript, case studies of physiologically based biopharmaceutics modeling (PBBM) applied to drug product quality are presented and summarized. These case studies exemplify a possible path to achieve an in vitro-in vivo link and encompass (a) development of biopredictive dissolution methods to support biowaivers, (b) model-informed formulation selection, (c) predicting clinical formulation performance, and (d) defining a safe space for regulatory flexibility via virtual bioequivalence (BE). Workflows for the development and verification of absorption models/PBBM and for the establishment of a safe space using dissolution as an input are described with examples. Breakout session discussions on topics, such as current challenges and some best practices in model development and verification, are included as part of the Supplementary material.

On May 15th–17th, 2017, the US FDA and the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) held a workshop at the University of Maryland’s Center of Excellence in Regulatory Science and Innovation (M-CERSI), to discuss the role of dissolution testing and translational modeling and simulation in enabling patient-centric solid oral drug product development. This 3-day event was attended by scientists from regulatory agencies, pharmaceutical companies, and academia. The workshop included podium presentations followed by breakout session discussions. The first day of the meeting focused on the challenges in dissolution method development and the role of dissolution testing throughout drug product development. On the second day, approaches to establish a link between in vitro testing and in vivo drug product performance (e.g., systemic exposure) were presented. Overall success rates and challenges in establishing IVIVCs via traditional and modern physiologically based pharmacokinetic (PBPK) modeling and simulation approaches were discussed. Day 3 provided an opportunity to discuss the expectations for establishing clinically relevant drug product specifications (CRDPS). It was recognized that understanding the impact of formulation and process variations on dissolution and in vivo performance is critical for most drug products formulated with poorly soluble drugs to ensure consistent product performance. The breakout sessions served as platforms for discussing controversial topics such as the clarification of dissolution terminology, PBPK model development and validation expectations, and approaches to set CRDPS. The meeting concluded with a commitment to continue the dialog between regulators, industry, and academia to advance overall product quality understanding.

Intraoral (IO) delivery is an alternative administration route to deliver a drug substance via the mouth that provides several advantages over conventional oral dosage forms. The purpose of this work was to develop and evaluate a novel, physiologically based oral cavity model for projection and mechanistic analysis of the clinical pharmacokinetics of intraoral formulations. The GastroPlus™ Oral Cavity Compartmental Absorption and Transit (OCCAT™) model was used to simulate the plasma concentration versus time profiles and the fraction and rate of intraoral drug transit/absorption for Intermezzo® sublingual tablets (zolpidem tartrate). The model was evaluated by the goodness-of-fit between simulated and observed concentrations and the deviation of key PK parameters (e.g., C max, T max, and AUC). In addition, a sensitivity analysis was conducted to demonstrate the interplay and impact of key modeling parameters on the fraction absorbed via oral mucosa (F a_IO). The OCCAT™ model captured the observed pharmacokinetics for Intermezzo® sublingual tablets (R 2 > 0.9). The predicted deviations (%) for C max, AUC0–inf, AUC0–20 min, and T max were 5.7, 28.0, 11.8, and 28.6%, respectively, indicating good prediction accuracy. The model also estimated ~18% of total drug was absorbed via the IO route. Furthermore, the sensitivity analysis indicated that the F a_IO was not only associated with drug diffusivity and unbound fraction in epithelium tissue (f ut) but also depended on the physicochemical properties of compounds for IO delivery (e.g., solubility and logD pH = 7.4). The novel physiologically based IO absorption OCCAT™ model showed satisfactory performance and will be helpful to guide development of future intraoral formulations.

Formulation changes are common during drug development either due to clinical or manufacturing considerations. These changes especially at later stages of drug development oftentimes raise questions on the potential impact of a new formulation on bioavailability. In this work, the preclinical assessment of formulation bridging risk for a Biopharmaceutics Classification System II development compound is presented. Early clinical studies were conducted using a liquid-filled capsule (LFC). To assess the feasibility of a conventional solid dosage form, an initial analysis was conducted using absorption modeling which indicated conventional formulation of micronized active pharmaceutical ingredient (API) could be a viable option. Subsequently, test formulations were prepared and tested in vivo in dogs. The solid formulations were able to match exposures of the LFC capsule in the dog model; in addition, a sensitivity to API PSD was observed in line with the modeling predictions. When tested in the clinic, the conventional solid formulation resulted in exposures of approximately 25% lower compared to the LFC on an equivalent dose basis; however, bridging with a small dose adjustment would be feasible. The outcome of the clinical study was better predicted by the modeling approach while the dog model appeared to somewhat overestimate absorption. Through the use of preclinical tools and modeling and simulation, a risk assessment around formulation bridging can be conducted and inform formulation decisions or subsequent clinical study designs.

Nanosuspensions, formulations based on the reduction of the active pharmaceutical ingredient (API) particle size in the sub-micron range and most typically around 100–200 nm, represent a valuable option for formulators to facilitate oral absorption of Biopharmaceutics Classification System class II and IV compounds. Their ability to increase the API dissolution rate and subsequent absorption and thus oral bioavailability has been demonstrated in preclinical and clinical settings. This review summarizes the current experience in the biopharmaceutic field with the use of nanosuspensions as oral delivery formulations. The principles behind nanosuspensions as well as the in vitro and in silico evaluation are discussed, while examples are presented highlighting both successes as well as limitations in their application as either toxicology or clinical formulations.

Based on adherence to intestinal mucosa, intralumenally administered liposomal formulations of 5-aminosalicylate (5-ASA) and 6-mercaptopurine (6-MP) were studied for their potential to enhance local drug delivery to intestinal tissue for the treatment of inflammatory bowel disease. 5-ASA was encapsulated in standard phospholipid liposomes while 6-MP required encapsulation in nonphospholipid liposomes to obtain equivalent drug loading. Encapsulation efficiency was measured by size-exclusion chromatography/high-performance liquid chromatogtaphy (HPLC). Liposomal formulations or solution of the drugs were injected into unligated jejunum to compare pharmacokinetics and into ligated loops of rat ileum and colon to evaluate local delivery. Dextran sulfate and acetic acid induced colitis were used as models of lower intestinal inflammation. Plasma, tissue and luminal drug and metabolite levels were measured by liquid scintillation counting or HPLC. Encapsulation efficiency of 6-MP was dependent on lipid content and composition. While liposomal encapsulation significantly reduced systemic absorption of 5-ASA this was not the case for 6-MP. Liposomal adherence to intestinal tissue resulted in increased tissue levels for 5-ASA; however, 6-MP local tissue levels were not improved compared to solution drug. Nonphospholipid liposomes optimize encapsulation of 6-MP. While liposomal formulations show potential for local drug delivery to diseased bowel, drug physicochemical properties, absorption, and metabolic profiles dictate tissue-targeting potential. Liposomes reduce systemic availability from paracellular absorption of hydrophilic 5-ASA, but fail to improve local tissue delivery of 6-MP, a molecule absorbed by passive membrane permeation that undergoes extensive first- pass metabolism.