Physiologically based absorption modelling to predict the impact of drug properties on pharmacokinetics of bitopertin

Developing a Formulation Strategy Coupled with PBPK Modeling and Simulation for the Weakly Basic Drug Albendazole

Albendazole (ABZ) is a weakly basic drug that undergoes extensive presystemic metabolism after oral administration and converts to its active form albendazole sulfoxide (ABZ_SO). The absorption of albendazole is limited by poor aqueous solubility, and dissolution is the rate-limiting step in the overall exposure of ABZ_SO. In this study, PBPK modeling was used to identify formulation-specific parameters that impact the oral bioavailability of ABZ_SO. In vitro experiments were carried out to determine pH solubility, precipitation kinetics, particle size distribution, and biorelevant solubility. A transfer experiment was conducted to determine the precipitation kinetics. A PBPK model for ABZ and ABZ_SO was developed using the Simcyp™ Simulator based on parameter estimates from in vitro experiments. Sensitivity analyses were performed to assess the impact of physiological parameters and formulation-related parameters on the systemic exposure of ABZ_SO. Model simulations predicted that increased gastric pH significantly reduced ABZ absorption and, subsequently, ABZ_SO systemic exposure. Reducing the particle size below 50 µm did not improve the bioavailability of ABZ. Modeling results illustrated that systemic exposure of ABZ_SO was enhanced by increasing solubility or supersaturation and decreasing the drug precipitation of ABZ at the intestinal pH level. These results were used to identify potential formulation strategies to enhance the oral bioavailability of ABZ_SO.

Pharmacokinetic profile of bitopertin, a selective GlyT1 inhibitor, in the rat

Bitopertin, a selective glycine transporter 1 (GlyT₁) inhibitor, has been extensively studied for the treatment of schizophrenia, with known safety and tolerability profiles in the clinic. Whereas several rodent experiments have been reported, the pharmacokinetic (PK) profile of bitopertin in rodents has not been extensively reported, as only two studies disclosed limited PK parameters in male rats after oral administration. Here, we determined the PK profile of bitopertin in female Sprague-Dawley rats. Blood samples were taken serially, before and after sub-cutaneous (0.03, 0.1, 0.3, 1, and 3 mg/kg) or intra-venous (0.1 mg/kg) administration. Plasma levels were determined by high-performance liquid chromatography coupled with heat-assisted electrospray ionisation tandem mass spectrometry (HPLC-HESI MS/MS). Subsequently, PK parameters were calculated using non-compartmental analysis, including area under the curve (AUC), time (T_max) to maximal plasma concentration (C_max), clearance (CL), volume of distribution (V_z), as well as half-life (T_1/2). Following sub-cutaneous injection, bitopertin exhibited dose-dependent AUC_0-∞ (439.6-34,018.9 ng/mL) and T_max (3.7-24.0 h), a very long terminal T_1/2 (35.06-110.32 h) and low CL (0.07-0.13 L/h/kg), suggesting that bitopertin is slowly absorbed and eliminated in the rat. The observed relationship between dose and the extent of drug exposure (AUC) was linear. Following administration of all sub-cutaneous doses, measured bitopertin plasma levels were comparable to levels achieved with doses already administered in the clinic. We hope that our results will be useful in the design of pre-clinical experiments in which this drug will eventually be administered sub-cutaneously.

The Use of Physiologically Based Pharmacokinetic Analyses-in Biopharmaceutics Applications -Regulatory and Industry Perspectives

The use of physiologically based pharmacokinetic (PBPK) modeling to support the drug product quality attributes, also known as physiologically based biopharmaceutics modeling (PBBM) is an evolving field and the interest in using PBBM is increasing. The US-FDA has emphasized on the use of patient centric quality standards and clinically relevant drug product specifications over the years. Establishing an in vitro in vivo link is an important step towards achieving the goal of patient centric quality standard. Such a link can aid in constructing a bioequivalence safe space and establishing clinically relevant drug product specifications. PBBM is an important tool to construct a safe space which can be used during the drug product development and lifecycle management. There are several advantages of using the PBBM approach, though there are also a few challenges, both with in vitro methods and in vivo understanding of drug absorption and disposition, that preclude using this approach and therefore further improvements are needed. In this review we have provided an overview of experience gained so far and the current perspective from regulatory and industry point of view. Collaboration between scientists from regulatory, industry and academic fields can further help to advance this field and deliver on promises that PBBM can offer towards establishing patient centric quality standards.

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.

Optimization of Spray-Drying Parameters for Formulation Development at Preclinical Scale

Spray-drying dispersion (SDD) is a well-established manufacturing technique used to prepare amorphous solid dispersions (ASDs), allowing for poorly soluble drugs to have improved bioavailability. However, the process of spray-drying with multiple factors and numerous variables can lead to a lengthy development timeline with intense resource requirements, which becomes the main obstacle limiting spray-drying development at the preclinical stage. The purpose of this work was to identify optimized preset parameters for spray-drying to support the early development of ASDs suitable for most circumstances rather than individual optimization. First, a mini-DoE (Design of Experiment) study was designed to evaluate the critical interplay of two key variables for spray-drying using a BUCHI B-290 mini spray dryer: solid load and atomizing spray gas flow. The critical quality attributes (CQAs) of the ASDs, including yield, particle size, morphology, and in vitro release profile, were taken into account to identify the impact of the key variables. The mini-DoE results indicated that a 5% solid load (w/v %) and 35 mm height atomizing spray gas flow were the most optimized parameters. These predefined values were further verified using different formulation compositions, including various polymers (Eudragit L100-55, HPMCAS-MF, PVAP, and PVP-VA64) and drugs (G-F, GEN-A, Indomethacin, and Griseofulvin), a range of drug loading (10 to 40%), and scale (200 mg to 200 g). Using these predefined parameters, all ASD formulations resulted in good yields as well as consistent particle size distribution. This was despite the differences in the formulations, making this a valuable and rapid approach ideal for early development. This strategy of leveraging the preset spray-drying parameters was able to successfully translate into a reproducible and efficient spray-drying platform while also saving material and reducing developmental timelines in early-stage formulation development.

Computational pharmaceutics - A new paradigm of drug delivery

In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still relies on traditional trial-and-error experiments, which are time-consuming, costly, and unpredictable. With the exponential growth of computing capability and algorithms, in recent ten years, a new discipline named "computational pharmaceutics" integrates with big data, artificial intelligence, and multi-scale modeling techniques into pharmaceutics, which offered great potential to shift the paradigm of drug delivery. Computational pharmaceutics can provide multi-scale lenses to pharmaceutical scientists, revealing physical, chemical, mathematical, and data-driven details ranging across pre-formulation studies, formulation screening, in vivo prediction in the human body, and precision medicine in the clinic. The present paper provides a comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling. We not only summarized the theories and progress of these technologies but also discussed the regulatory requirements, current challenges, and future perspectives in the area, such as talent training and a culture change in the future pharmaceutical industry.

Towards virtual bioequivalence studies for oral dosage forms containing poorly water-soluble drugs: a physiologically based biopharmaceutics modeling (PBBM) approach

The objective of the present study was to develop a physiologically based biopharmaceutics (PBBM) approach to predict the bioequivalence of dosage forms containing poorly soluble drugs. Aripiprazole and enzalutamide were used as model drugs. Variations in the gastrointestinal (GI) physiological parameters of fasted humans were taken into consideration in in vitro biorelevant dissolution testing and in an in silico PBBM simulations. To estimate bioequivalence between dosage forms, the inter-individual variabilities in their performance in virtual human subjects were predicted from the in vitro studies and variability in e.g. gastric emptying and fluid volume in the stomach was also taken into account. Formulations with different in vitro dissolution performance, a solution and a tablet formulation, were used in order to evaluate the accuracy of bioequivalence prediction using the PBBM approach. The bioequivalence parameters, i.e. geometric mean ratio and 90% confidence interval, for both drugs were predicted well in the virtual studies. In order to achieve even more precise predictions, it will be important to continue characterizing GI physiological parameters, along with their variabilities, on both an inter-subject and inter-occasion basis.

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

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.

Prediction of fasted and fed bioequivalence for immediate release drug products using physiologically based biopharmaceutics modeling (PBBM)

Bioequivalence studies are an integral part of clinical pharmacology strategy for drug development. Physiologically based biopharmaceutics modeling (PBBM) can be a helpful tool to assess potential bioequivalence risks and predict the outcome of bioequivalence studies. In this study, GastroPlus™ was used for virtual bioequivalence (VBE) assessment of 6 case studies which includes four BCS 2, and one each of BCS 1 and 3 molecules. The purpose was to investigate if bioequivalence in fed state can be accurately predicted based on model developed on data from bioequivalence study in fasted state and known food effect from clinical studies. Our results show that we were able to successfully predict passing (5 cases) and failed (1 case) bioequivalence studies. Ultimately, if there is confidence in such models, a case can be made to waive fed bioequivalence study, on a case-by-case basis (e.g. for BCS class 1 and 2 molecules with known food effect mechanism, reliable estimate of human pharmacokinetic parameters, and available in vivo data in fasted state for model verification). This has the potential to reduce clinical burden in drug development, increase confidence in pivotal BE studies and support regulatory applications such as justify waiving of BE study for Scale-Up and Post Approval Changes (SUPAC). Hence VBE can significantly reduce time and cost of drug development, as well as minimize drug exposure to healthy volunteers.

Current State and Future Expectations of Translational Modeling Strategies to Support Drug Product Development, Manufacturing Changes and Controls: A Workshop Summary Report

The implementation of clinically relevant drug product specifications (CRDPS) depends on establishing a link between in vitro performance and in vivo exposure. The scientific community, including regulatory agencies, relies on biopharmaceutics tools on the in vitro performance side, while to enable the link to in vivo exposure, physiologically based pharmacokinetic (PBPK) modeling offers much promise. However, when it comes to PBPK applications in support of CRDPS, otherwise called physiologically based biopharmaceutics models (PBBM), the tools are not yet at the desired level. Currently, it is not possible to integrate detailed variations in chemistry, manufacturing and controls (CMC) attributes and parameters into these models in a way that can consistently predict their effect on local and systemic drug exposure. Specifically, to achieve the desired level, there is a need to advance the science and policy of PBBM. This manuscript summarizes the proceedings of a three-day workshop where the following themes were discussed: 1) Challenges in the development and implementation of in vitro biopredictive tools needed for successful mechanistic modeling; 2) Best practices in model development, verification and validation; and 3) Appropriate terminology (e.g., PBBM vs. PBPK models for biopharmaceutics applications) and applications of PBBM in support of drug product quality.

Food Effect Projections via Physiologically Based Pharmacokinetic Modeling: Predictive Case Studies

Food can alter the absorption of orally administered drugs. Biopharmaceutics physiologically based pharmacokinetic (PBPK) modeling offers the possibility to simulate a compound's pharmacokinetics under fasted or fed states. To advance the utility of PBPK modeling, with a view to regulatory impact, we have pooled our experience across 4 pharmaceutical companies to propose a general multistep PBPK workflow leveraging pre-existing clinical data for immediate-release formulations of Biopharmaceutics Classification System I and II compounds. With this strategy, we wish to promote pragmatic PBPK approaches for compounds where absorption is well understood, that is, compounds with moderate-to-high permeability that are not substrates for uptake transporters. Five case studies demonstrate how food effect can be well predicted using appropriately established and validated models. The case studies integrate solubility and dissolution data for initial model development and apply a "middle-out" validation with clinical data in one prandial state. Then, whenever possible, a validation against both fasted and fed state data is recommended before application of the models prospectively for to-be-marketed formulations. Thus, when combined with limited clinical data, PBPK models could be used to simulate outcomes for new doses, formulations, or active pharmaceutical ingredient forms, in lieu of a clinical food-effect study.

Combining 'Bottom-Up' and 'Top-Down' Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example

Background and Objectives

We propose a strategy for studying ethnopharmacology by conducting sequential physiologically based pharmacokinetic (PBPK) prediction (a ‘bottom-up’ approach) and population pharmacokinetic (popPK) confirmation (a ‘top-down’ approach), or in reverse order, depending on whether the purpose is ethnic effect assessment for a new molecular entity under development or a tool for ethnic sensitivity prediction for a given pathway. The strategy is exemplified with bitopertin.

Methods

A PBPK model was built using Simcyp^® to simulate the pharmacokinetics of bitopertin and to predict the ethnic sensitivity in clearance, given pharmacokinetic data in just one ethnicity. Subsequently, a popPK model was built using NONMEM^® to assess the effect of ethnicity on clearance, using human data from multiple ethnic groups. A comparison was made to confirm the PBPK-based ethnic sensitivity prediction, using the results of the popPK analysis.

Results

PBPK modelling predicted that the bitopertin geometric mean clearance values after 20 mg oral administration in Caucasians would be 1.32-fold and 1.27-fold higher than the values in Chinese and Japanese, respectively. The ratios of typical clearance in Caucasians to the values in Chinese and Japanese estimated by popPK analysis were 1.20 and 1.17, respectively. The popPK analysis results were similar to the PBPK modelling results.

Conclusion

As a general framework, we propose that PBPK modelling should be considered to predict ethnic sensitivity of pharmacokinetics prior to any human data and/or with data in only one ethnicity. In some cases, this will be sufficient to guide initial dose selection in different ethnicities. After clinical trials in different ethnicities, popPK analysis can be used to confirm ethnic differences and to support dose justification and labelling. PBPK modelling prediction and popPK analysis confirmation can complement each other to assess ethnic differences in pharmacokinetics at different drug development stages.

Electronic supplementary material

The online version of this article (doi:10.1007/s40262-015-0356-1) contains supplementary material, which is available to authorized users.

Symptomatic thinking: the current state of Phase III and IV clinical trials for cognition in schizophrenia

Research indicates that relieving the cognitive and negative symptoms of schizophrenia is crucial for improving patient quality of life. However effective pharmacotherapies for cognitive and negative symptoms do not currently exist. A review of ongoing Phase III clinical trials indicates that, despite numerous compounds being investigated for cognition in schizophrenia, few are actually novel and most are not backed by empirically driven preclinical research efforts. Based on these trials, and a general disinvestment in development of novel therapies for schizophrenia, the likelihood of a major advancement in treating cognitive differences in schizophrenia does not look promising. Possible ways in which the remaining resources for development of novel treatment for schizophrenia can best be leveraged are discussed.

Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities

PURPOSE OF REVIEW

This review gives a perspective on the current "state of the art" in metabolic drug-drug interaction (DDI) prediction. We highlight areas of successful prediction and illustrate progress in areas where limits in scientific knowledge or technologies prevent us from having full confidence.

RECENT FINDINGS

Several examples of success are highlighted. Work done for bitopertin shows how in vitro and clinical data can be integrated to give a model-based understanding of pharmacokinetics and drug interactions. The use of interpolative predictions to derive explicit dosage recommendations for untested DDIs is discussed using the example of ibrutinib, and the use of DDI predictions in lieu of clinical studies in new drug application packages is exemplified with eliglustat and alectinib. Alectinib is also an interesting case where dose adjustment is unnecessary as the activity of a major metabolite compensates sufficiently for changes in parent drug exposure. Examples where "unusual" cytochrome P450 (CYP) and non-CYP enzymes are responsible for metabolic clearance have shown the importance of continuing to develop our repertoire of in vitro regents and techniques. The time-dependent inhibition assay using human hepatocytes suspended in full plasma allowed improved DDI predictions, illustrating the importance of continued in vitro assay development and refinement.

SUMMARY

During the past 10 years, a highly mechanistic understanding has been developed in the area of CYP-mediated metabolic DDIs enabling the prediction of clinical outcome based on preclinical studies. The combination of good quality in vitro data and physiologically based pharmacokinetic modeling may now be used to evaluate DDI risk prospectively and are increasingly accepted in lieu of dedicated clinical studies.

Effects of Cytochrome P450 3A4 Inhibitors-Ketoconazole and Erythromycin-on Bitopertin Pharmacokinetics and Comparison with Physiologically Based Modelling Predictions

OBJECTIVE

To assess the effect of strong and moderate cytochrome P450 (CYP) 3A4 inhibition on exposure of bitopertin, a glycine reuptake inhibitor primarily metabolized by CYP3A4, and to compare the results with predictions based on physiologically based pharmacokinetic (PBPK) modelling.

METHODS

The effects of ketoconazole and erythromycin were assessed in two male volunteer studies with open-label, two-period, fixed-sequence designs. Twelve subjects were enrolled in each of the studies. In period 1, a single dose of bitopertin was administered; in period 2, 400 mg ketoconazole was administered once daily for 17 days or 500 mg erythromycin was administered twice daily for 21 days. A single dose of bitopertin was coadministered on day 5. Pharmacokinetic parameters were derived by non-compartmental methods. Simulated bitopertin profiles using dynamic PBPK modelling for a typical healthy volunteer in GastroPlus(®) were used to predict changes in pharmacokinetic parameters.

RESULTS

In healthy volunteers, coadministration of ketoconazole increased the bitopertin area under the plasma concentration-time curve (AUC) from 0 to 312 h (AUC0-312h) 4.2-fold (90 % confidence interval [CI] 3.5-5.0) and erythromycin increased the AUC from time zero to infinity (AUC0-inf) 2.1-fold (90 % CI 1.9-2.3). The peak concentration (C max) increased by <25 % in both studies. Simulated bitopertin profiles using PBPK modelling showed good agreement with the observed AUC ratios in both studies. The predicted AUC0-inf ratios for the interaction with ketoconazole and erythromycin were 7.7 and 1.9, respectively.

CONCLUSION

Strong CYP3A4 inhibitors increase AUC0-inf of bitopertin 7- to 8-fold and hence should not be administered concomitantly with bitopertin. Moderate CYP3A4 inhibitors double AUC0-inf.

Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine

Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

Physiologically Based Absorption Modeling to Explore the Impact of Food and Gastric pH Changes on the Pharmacokinetics of Alectinib

Alectinib, a lipophilic, basic, anaplastic lymphoma kinase (ALK) inhibitor with very low aqueous solubility, has received Food and Drug Administration-accelerated approval for the treatment of patients with ALK+ non-small-cell lung cancer. This paper describes the application of physiologically based absorption modeling during clinical development to predict and understand the impact of food and gastric pH changes on alectinib absorption. The GastroPlus^™ software was used to develop an absorption model integrating in vitro and in silico data on drug substance properties. Oral pharmacokinetics was simulated by linking the absorption model to a disposition model fit to pharmacokinetic data obtained after an intravenous infusion. Simulations were compared to clinical data from a food effect study and a drug-drug interaction study with esomeprazole, a gastric acid-reducing agent. Prospective predictions of a positive food effect and negligible impact of gastric pH elevation were confirmed with clinical data, although the exact magnitude of the food effect could not be predicted with confidence. After optimization of the absorption model with clinical food effect data, a refined model was further applied to derive recommendations on the timing of dose administration with respect to a meal. The application of biopharmaceutical absorption modeling is an area with great potential to further streamline late stage drug development and with impact on regulatory questions.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Application of Absorption Modeling in Rational Design of Drug Product Under Quality-by-Design Paradigm

Physiologically based absorption models can be an important tool in understanding product performance and hence implementation of Quality by Design (QbD) in drug product development. In this report, we show several case studies to demonstrate the potential application of absorption modeling in rational design of drug product under the QbD paradigm. The examples include application of absorption modeling—(1) prior to first-in-human studies to guide development of a formulation with minimal sensitivity to higher gastric pH and hence reduced interaction when co-administered with PPIs and/or H2RAs, (2) design of a controlled release formulation with optimal release rate to meet trough plasma concentrations and enable QD dosing, (3) understanding the impact of API particle size distribution on tablet bioavailability and guide formulation design in late-stage development, (4) assess impact of API phase change on product performance to guide specification setting, and (5) investigate the effect of dissolution rate changes on formulation bioperformance and enable appropriate specification setting. These case studies are meant to highlight the utility of physiologically based absorption modeling in gaining a thorough understanding of the product performance and the critical factors impacting performance to drive design of a robust drug product that would deliver the optimal benefit to the patients.