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

Oral absorption from surfactant-based drug formulations: the impact of molecularly dissolved drug on bioavailability

Enabling drug formulations are often required to ensure sufficient absorption after oral administration of poorly soluble drugs. While these formulations typically increase the apparent solubility of the drug, it is widely acknowledged that only molecularly dissolved, i.e. free fraction of the drug, is prone for direct absorption, while colloid-associated drug does not permeate to the same extent. In the present study, we aimed at comparing the effect of molecularly and apparently (i.e., the sum of molecularly and colloid-associated drug) dissolved drug concentrations on the oral absorption of a poorly water-soluble drug compound, Alectinib. Mixtures of Alectinib and respectively 50%, 25%, 12.5%, and 3% sodium lauryl sulfate (SLS) relative to the dose were prepared and small-scale dissolution tests were performed under simulated fed and fasted state conditions. Both the molecularly and apparently dissolved drug concentrations were assessed in parallel using microdialysis and centrifugation/filtration sampling, respectively. The data served as the basis for an in vitro-in vivo correlation (IVIVC) and as input for a GastroPlusTM physiologically based biopharmaceutics model (PBBM). It was shown that with increasing the content of SLS the apparently dissolved drug in FeSSIF and FaSSIF increased to a linear extent and thus, the predicted in vivo performance of the 50% SLS formulation, based on apparently dissolved drug, would outperform all other formulations. Against common expectation, however, the free (molecularly dissolved) drug concentrations were found to vary with SLS concentrations as well, yet to a minor extent. A systematic comparison of solubilized and free drug dissolution patterns at different SLS contents of the formulations and prandial states allowed for interesting insights into the complex dissolution- / supersaturation-, micellization-, and precipitation-behavior of the formulations. When comparing the in vitro datasets with human pharmacokinetic data from a bioequivalence study, it was shown that the use of molecularly dissolved drug resulted in an improved IVIVC. By incorporating the in vitro dissolution datasets into the GastroPlusTM PBBM, the apparently dissolved drug concentrations resulted in both, a remarkable overprediction of plasma concentrations as well as a misprediction of the influence of SLS on systemic exposure. In contrast, by using the molecularly dissolved drug (i.e., free fraction) as the model input, the predicted plasma concentration-time profiles were in excellent agreement with observed data for all formulations under both fed and fasted conditions. By combining an advanced in vitro assessment with PBBM, the present study confirmed that only the molecularly dissolved drug, and not the colloid-associated drug, is available for direct absorption.

Assessing and mitigating pH-mediated DDI risks in drug development - formulation approaches and clinical considerations

pH-mediated drug-drug interactions (DDI) is a prevalent DDI in drug development, especially for weak base compounds with highly pH-dependent solubility. FDA has released a guidance on the evaluation of pH-mediated DDI assessments using in vitro testing and clinical studies. Currently, there is no common practice of ways of testing across the academia and industry. The development of biopredictive method and physiologically-based biopharmaceutics modeling (PBBM) approaches to assess acid-reducing agent (ARA)-DDI have been proven with accurate prediction and could decrease drug development burden, inform clinical design and potentially waive clinical studies. Formulation strategies and careful clinical design could help mitigate the pH-mediated DDI to avoid more clinical studies and label restrictions, ultimately benefiting the patient. In this review paper, a detailed introduction on biorelevant dissolution testing, preclinical and clinical study requirement and PBPK modeling approaches to assess ARA-DDI are described. An improved decision tree for pH-mediated DDI is proposed. Potential mitigations including clinical or formulation strategies are discussed.

Physiologically Based Pharmacokinetic Absorption Model for Pexidartinib to Evaluate the Impact of Meal Contents and Intake Timing on Drug Exposure

Pexidartinib is a systemic treatment for patients with tenosynovial giant cell tumor not amenable to surgery. Oral absorption of pexidartinib is affected by food; administration with a high-fat meal (HFM) or low-fat meal (LFM) increases absorption by approximately 100% and approximately 60%, respectively, compared with the fasted state. Pexidartinib is currently dosed 250 mg orally twice daily with an LFM (approximately 11-14 g of total fat). We developed a physiologically based pharmacokinetic model to determine the impact on drug exposure of dose timing with respect to meals, meal type, and caloric content. A 15%-16% increase in plasma exposure was predicted when consuming an HFM 1 hour after dosing with an LFM, but almost no effect on pharmacokinetics was predicted when an HFM was consumed 3 hours or more before or after pexidartinib dosing with an LFM. Exposure was not significantly affected when pexidartinib was taken with a 500-kcal LFM over the range of fat (approximately 11-14 g of total fat; 20%-25% calories from fat) for an LFM. These findings on timing of pexidartinib dose with respect to meals should be considered by patients and physicians to reduce the potential for side effects.

Effects of H2-Receptor Antagonists on the Exposure of Dacomitinib

Dacomitinib is an irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor indicated for the treatment of patients with advanced non-small-cell lung cancer (NSCLC) and EGFR-activating mutations. Proton-pump inhibitors decreased dacomitinib exposure. This analysis summarizes the effect of Histamine-2 receptor antagonists (H2RAs) on dacomitinib exposure. A within-patient comparison of the steady-state trough concentrations (Ctrough,ss) of dacomitinib and its active metabolite and active moiety with and without concomitant use of H2RAs was conducted using a linear mixed effects model with pooled data from 11 clinical studies in patients with NSCLC. An oral absorption physiologically based pharmacokinetic (PBPK) model was constructed and verified using clinical pharmacokinetic (PK) data after a single dose of dacomitinib in healthy volunteers to estimate the effect of gastric pH altered by an H2RA on dacomitinib's PKs. The adjusted geometric mean of the dacomitinib Ctrough,ss of the dacomitinib parent, metabolite and active moiety following co-administration with an H2RA was approximately 86%, 104% and 100% relative to that following dacomitinib 45 mg administration without an H2RA (p > 0.05). The PBPK modeling showed negligible change in dacomitinib maximum concentration (Cmax) and area under the drug concentration-time curve (AUC) over 0-24 h after H2RA administration when compared with those administered dacomitinib alone. Co-administration of an H2RA with dacomitinib is not expected to have any clinically relevant effect on dacomitinib exposure.

Prediction of pharmacokinetics of an anaplastic lymphoma kinase inhibitor in rat and monkey: application of physiologically based pharmacokinetic model as an alternative tool to minimise animal studies

The pharmacokinetic (PK) and toxicokinetic profile of a drug from its preclinical evaluation helps the researcher determine whether the drug should be tested in humans based on its safety and toxicity.Preclinical studies require time and resources and are prone to error. Moreover, according to the United States Food and Drug Administration Modernisation Act 2, animal testing is no longer mandatory for new drug development, and an animal-free alternative, such as cell-based assay and computer models, can be used.Different physiologically based PK models were developed for an anaplastic lymphoma kinase inhibitor in rats and monkeys after intravenous and oral administration using its physicochemical properties and in vitro characterisation data.The developed model was validated against the in vivo data available in the literature, and the validation results were found within the acceptable limit. A parameter sensitivity analysis was performed to identify the properties of the compound influencing the PK profile.This work demonstrates the application of the physiologically based PK model to predict the PKs of a drug, which will eventually assist in reducing the number of animal studies and save time and cost of drug discovery and development.

Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs

INTRODUCTION

Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs.

METHOD

Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed.

RESULT

From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively.

CONCLUSION

PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.

Physiologically based pharmacokinetic (PBPK) modelling of oral drug absorption in older adults - an AGePOP review

The older population consisting of persons aged 65 years or older is the fastest-growing population group and also the major consumer of pharmaceutical products. Due to the heterogenous ageing process, this age group shows high interindividual variability in the dose-exposure-response relationship and, thus, a prediction of drug safety and efficacy is challenging. Although physiologically based pharmacokinetic (PBPK) modelling is a well-established tool to inform and confirm drug dosing strategies during drug development for special population groups, age-related changes in absorption are poorly accounted for in current PBPK models. The purpose of this review is to summarise the current state-of-knowledge in terms of physiological changes with increasing age that can influence the oral absorption of dosage forms. The capacity of common PBPK platforms to incorporate these changes and describe the older population is also discussed, as well as the implications of extrinsic factors such as drug-drug interactions associated with polypharmacy on the model development process. The future potential of this field will rely on addressing the gaps identified in this article, which can subsequently supplement in-vitro and in-vivo data for more robust decision-making on the adequacy of the formulation for use in older adults and inform pharmacotherapy.

Prediction of gastric pH-mediated drug exposure using physiologically-based pharmacokinetic modeling: A case study of itraconazole

Abnormal gastric acidity, including achlorhydria, can act as a significant source of variability in orally administered drugs especially with pH-sensitive solubility profiles, such as weak bases, potentially resulting in an undesirable therapeutic response. This study aimed to evaluate the utility of physiologically-based pharmacokinetic (PBPK) modeling in the prediction of gastric pH-mediated drug exposure by using itraconazole, a weak base, as a case. An itraconazole PBPK model was developed on the mechanistic basis of its absorption kinetics in a middle-out manner from a stepwise in vitro-in vivo extrapolation to in vivo refinement. Afterward, an independent prospective clinical study evaluating gastric pH and itraconazole pharmacokinetics (PKs) under normal gastric acidity and esomeprazole-induced gastric hypoacidity was conducted for model validation. Validation was performed by comparing the predicted data with the clinical observations, and the valid model was subsequently applied to predict PK changes under achlorhydria. The developed itraconazole PBPK model showed reasonable reproducibility for gastric pH-mediated exposure observed in the clinical investigation. Based on the model-based simulations, itraconazole exposure was expected to be decreased up to 65% under achlorhydria, and furthermore, gastric pH-mediated exposure could be mechanistically interpreted according to sequential variation in total solubility, dissolution, and absorption. This study suggested the utility of PBPK modeling in the prediction of gastric pH-mediated exposure, especially for drugs whose absorption is susceptible to gastric pH. Our findings will serve as a leading model for further mechanistic assessment of exposure depending on gastric pH for various drugs, ultimately contributing to personalized pharmacotherapy.

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.

Development of Alectinib-Suspended SNEDDS for Enhanced Solubility and Dissolution

Alectinib hydrochloride (ALH), a tyrosine kinase inhibitor, is a practically water-insoluble drug classified as BCS class IV. The present study aimed to develop novel suspended self-nanoemulsifying drug delivery system (Su-SNEDDS) to enhance the solubility and dissolution rate. The Su-SNEDDS was prepared by saturation and suspension of ALH in SNEDDS with ultrasonication energy. According to evaluation by the dispersion test and the results of particle size analysis, the selected SNEDDS composed of Kolliphor HS 15 and Capmul MCM C8 as surfactant and oil, respectively, showed a complete dissolution within 30 min. However, the SNEDDS loaded and solubilized only small amount of ALH (<0.6%, w/w). On the other hand, 10% ALH-loaded Su-SNEDDS containing small and micronized ALH particles of <5 μm had about 20-fold higher ALH-loading% than the SNEDDS and reached a 100% dissolution rate within 30 min in 1% sodium lauryl sulfate (SLS) pH 1.2 buffer. In the dispersion test and microscopic observation, micronized ALH particles in the Su-SNEDDS were readily dispersed in the dissolution medium with spontaneous nanoemulsion formation and instantly solubilized with the aid of SLS. Taken together, our results suggest that the Su-SNEDDS would be a potent oral dosage form to enhance the solubilization and dissolution rate of ALH in a new technological way.

Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective

Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.

Enteral administration of alectinib for ALK-positive non-small cell lung cancer in an elderly patient: A case report

RATIONALE

Alectinib is a tyrosine kinase inhibitor (TKI) approved for use as first-line metastatic therapy for patients with anaplastic lymphoma kinase-rearranged non-small cell lung cancer. Certain medical conditions related to the tumor lesions may not allow oral administration of TKIs.

PATIENT CONCERNS

We hereby report the case of a 90-year-old patient with anaplasic lymphoma kinase-rearranged lung cancer with severely impaired general condition and swallowing disorders.

DIAGNOSIS

A thoracic computerized tomography (CT)-scan confirmed the presence of a mediastinal tumor lesion explaining the swallowing disorders secondary to recurrent paralysis.

INTERVENTIONS

As no oral administration was feasible, alectinib was administered by percutaneous gastrostomy.

OUTCOMES

The patient had few side-effects. He presented a major clinical and radiological response. After 2 months of treatment with alectinib, his mini-mental state examination had increased from 8/30 to 23/30. He had a 60% reduction in targeted pulmonary, bone and node lesions according to the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1). After 6 months of treatment, the patient's performance status had evolved from 3 to 1. This improvement in general condition made it possible to remove the feeding tube.

LESSONS

In cases of lung cancer with oncogenic addiction, enteral administration of TKIs should be considered for elderly patients with an impaired general condition.

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

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

Comprehensive PBPK model to predict drug interaction potential of Zanubrutinib as a victim or perpetrator

A physiologically based pharmacokinetic (PBPK) model was developed to evaluate and predict (1) the effect of concomitant cytochrome P450 3A (CYP3A) inhibitors or inducers on the exposures of zanubrutinib, (2) the effect of zanubrutinib on the exposures of CYP3A4, CYP2C8, and CYP2B6 substrates, and (3) the impact of gastric pH changes on the pharmacokinetics of zanubrutinib. The model was developed based on physicochemical and in vitro parameters, as well as clinical data, including pharmacokinetic data in patients with B-cell malignancies and in healthy volunteers from two clinical drug-drug interaction (DDI) studies of zanubrutinib as a victim of CYP modulators (itraconazole, rifampicin) or a perpetrator (midazolam). This PBPK model was successfully validated to describe the observed plasma concentrations and clinical DDIs of zanubrutinib. Model predictions were generally within 1.5-fold of the observed clinical data. The PBPK model was used to predict untested clinical scenarios; these simulations indicated that strong, moderate, and mild CYP3A inhibitors may increase zanubrutinib exposures by approximately four-fold, two- to three-fold, and <1.5-fold, respectively. Strong and moderate CYP3A inducers may decrease zanubrutinib exposures by two- to three-fold or greater. The PBPK simulations showed that clinically relevant concentrations of zanubrutinib, as a DDI perpetrator, would have no or limited impact on the enzyme activity of CYP2B6 and CYP2C8. Simulations indicated that zanubrutinib exposures are not impacted by acid-reducing agents. Development of a PBPK model for zanubrutinib as a DDI victim and perpetrator in parallel can increase confidence in PBPK models supporting zanubrutinib label dose recommendations.

Population Pharmacokinetic Analysis of the BTK Inhibitor Zanubrutinib in Healthy Volunteers and Patients With B-Cell Malignancies

Zanubrutinib is a potent, second-generation Bruton's tyrosine kinase inhibitor that is currently being investigated in patients with B-cell malignancies and recently received accelerated approval in the United States for treatment of relapsed/refractory mantle cell lymphoma. The objective of this analysis was to develop a population pharmacokinetic (PK) model to characterize the PKs of zanubrutinib and identify the potential impact of intrinsic and extrinsic covariates on zanubrutinib PK. Data across nine clinical studies of patients with B-cell malignancies and data of healthy volunteers (HVs) were included in this analysis, at total daily doses ranging from 20 to 320 mg. In total, 4,925 zanubrutinib plasma samples from 632 subjects were analyzed using nonlinear mixed-effects modeling. Zanubrutinib PKs were adequately described by a two-compartment model with sequential zero-order then first-order absorption, and first-order elimination. A time-dependent residual error model was implemented in order to better capture the observed maximum concentration variability in subjects. Baseline alanine aminotransferase and health status (HVs or patients with B-cell malignancies) were identified as statistically significant covariates on the PKs of zanubrutinib. These factors are unlikely to be clinically meaningful based on a sensitivity analysis. No statistically significant differences in the PKs of zanubrutinib were observed based on age, sex, race (Asian, white, and other), body weight, mild or moderate renal impairment (creatinine clearance ≥ 30 mL/minute as estimated by Cockcroft-Gault), baseline aspartate aminotransferase, bilirubin, tumor type, or use of acid-reducing agents (including proton pump inhibitors). These results support that no dose adjustment is considered necessary based on the aforementioned factors.

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.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-in-human prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

Application of Physiologically-Based Pharmacokinetic Modeling to Predict Gastric pH-Dependent Drug-Drug Interactions for Weak Base Drugs

Weak‐base drugs are susceptible to drug–drug interactions (DDIs) when coadministered with gastric acid–reducing agents (ARAs). We developed PBPK models to evaluate the potential of such pH‐dependent DDIs for four weak‐base drugs, i.e., tapentadol, darunavir, erlotinib, and saxagliptin. The physiologically‐based pharmacokinetic (PBPK) models of these drugs were first optimized using pharmacokinetic (PK) data following oral administration without ARAs, which were then verified with data from additional PK studies in the presence and absence of food. The models were subsequently used to predict the extent of DDIs with ARA coadministration. Sensitivity analysis was conducted to explore the impact of gastric pH on quantitative prediction of drug exposure in the presence of ARA. The results suggested that the PBPK models developed could adequately describe the lack of the effect of ARA on the PK of tapentadol, darunavir, and saxagliptin and could qualitatively predict the effect of ARA in reducing the absorption of erlotinib. Further studies involving more drugs with positive pH‐dependent DDIs are needed to confirm the findings and broaden our knowledge base to further improve the utilization of PBPK modeling to evaluate pH‐dependent DDI potential.

Pharmacokinetic-Based Drug-Drug Interactions with Anaplastic Lymphoma Kinase Inhibitors: A Review

Anaplastic lymphoma kinase (ALK) inhibitors are important treatment options for non-small-cell lung cancer (NSCLC), associated with ALK gene rearrangement. Patients with ALK gene rearrangement show sensitivity to and benefit clinically from treatment with ALK tyrosine kinase inhibitors (ALK-TKIs). To date, crizotinib, ceritinib, alectinib, brigatinib, lorlatinib, and entrectinib have received approval from the US Food and Drug Administration and/or the European Medicines Agency for use during the treatment of ALK-gene-rearrangement forms of NSCLC. Although the oral route of administration is convenient and results in good compliance among patients, oral administration can be affected by many factors, such as food, intragastric pH, cytochrome P450 enzymes, transporters, and p-glycoprotein. These factors can result in increased risks for serious adverse events or can lead to reduced therapeutic effects of ALK-TKIs. This review characterizes and summarizes the pharmacokinetic parameters and drug–-drug interactions associated with ALK-TKIs to provide specific recommendations for oncologists and clinical pharmacists when prescribing ALK-TKIs.

Combination of Gemcitabine with Cell-Penetrating Peptides: A Pharmacokinetic Approach Using In Silico Tools

Gemcitabine is an anticancer drug used to treat a wide range of solid tumors and is a first line treatment for pancreatic cancer. Our group has previously developed novel conjugates of gemcitabine with cell-penetrating peptides (CPP), and here we report some preliminary data regarding the pharmacokinetics of gemcitabine, two gemcitabine-CPP conjugates and respective CPP gathered from GastroPlus™, and analyze these results considering our previous evaluation of gemcitabine release and conjugates’ bioactivity. Additionally, seeking to shed some light on the relation between the penetration ability of CPP and their physicochemical properties, chemical descriptors for the 20 natural amino acids were calculated, a new principal property scale (z-scale) was created and CPP prediction models were developed, establishing quantitative structure-activity relationships (QSAR). The z-scores of the peptides conjugated with gemcitabine are presented and analyzed with the aforementioned data.

PBPK Absorption Modeling of Food Effect and Bioequivalence in Fed State for Two Formulations with Crystalline and Amorphous Forms of BCS 2 Class Drug in Generic Drug Development

Prediction of the effect of food on drug's pharmacokinetics using modeling and simulation could cause difficulties due to complex in vivo processes. A generic formulation with amorphous form of BCS 2 class drug substance was developed and compared in vitro and in vivo to the reference drug product with drug substance in crystalline form. In order to approve generic formulation, some regulatory agencies are requesting to perform bioequivalence (BE) studies also in fed state. Food can have various effects on drug dissolution and absorption, depending also on drug's properties. A physiologically based pharmacokinetic (PBPK) absorption model was built in GastroPlus™ to predict the food effect on generic and reference formulation and to predict the fed BE study outcome. During model development, we were searching for model inputs that impact and describe in vivo behavior of amorphous and crystalline forms of active pharmaceutical ingredient (API) in fast and fed conditions. The developed model was able to predict the food effect with up to 10% prediction error (PE). Performed virtual BE trials confirmed the BE of drug products in fed state. Our model was able to capture the difference between the two drug products containing different forms of API (amorphous and crystalline) and predict the food effect on both formulations.

Physiologically Based Absorption Modeling of Salts of Weak Bases Based on Data in Hypochlorhydric and Achlorhydric Biorelevant Media

Physiologically based absorption modeling has been attracting increased attention to study the interactions of weakly basic drug compounds with acid-reducing agents like proton-pump inhibitors and H2 blockers. Recently, standardized gastric and intestinal biorelevant media to simulate the achlorhydric and hypochlorhydric stomach were proposed and solubility and dissolution data for two model compounds were generated. In the current manuscript, for the first time, we report the utility of these recently proposed biorelevant media as input into physiologically based absorption modeling. Where needed, data collected with the biorelevant gastrointestinal transfer (BioGIT) system were used for informing the simulations in regard to the precipitation kinetics. Using two model compounds, a HCl salt and a semi-fumarate co-crystal which as expected dissolve to a greater extent in these media (and in gastric and intestinal human aspirates) compared to what the pH-solubility profile of the free form would suggest, we demonstrate successful description of the plasma concentration profiles and correctly predicted the lack of significant interaction after administration with pantoprazole or famotidine, respectively. Thus, the data reported in this manuscript represent an initial step towards defining biorelevant input for such simulations on interactions with acid-reducing agents.

Effect of Hepatic Impairment on the Pharmacokinetics of Alectinib

Alectinib is approved and recommended as the preferred first‐line treatment for patients with anaplastic lymphoma kinase (ALK)‐positive non–small cell lung cancer. The effect of hepatic impairment on the pharmacokinetics (PK) of alectinib was assessed with physiologically based PK modeling prospectively and in a clinical study. An open‐label study (NCT02621047) investigated a single 300‐mg dose of alectinib in moderate (n = 8) and severe (n = 8) hepatic impairment (Child‐Pugh B/C), and healthy subjects (n = 12) matched for age, sex, and body weight. Physiologically based PK modeling was conducted prospectively to inform the clinical study design and support the use of a lower dose and extended PK sampling in the study. PK parameters were calculated for alectinib, its major similarly active metabolite, M4, and the combined exposure of alectinib and M4. Unbound concentrations were assessed at 6 and 12 hours postdose. Administration of alectinib to subjects with hepatic impairment increased the area under the plasma concentration–time curve from time 0 to infinity of the combined exposure of alectinib and M4 to 136% (90% confidence interval [CI], 94.7‐196) and 176% (90%CI 98.4‐315), for moderate and severe hepatic impairment, respectively, relative to matched healthy subjects. Unbound concentrations for alectinib and M4 did not appear substantially different between hepatic‐impaired and healthy subjects. Moderate hepatic impairment had only a modest, not clinically significant effect on alectinib exposure, while the higher exposure observed in severe hepatic impairment supports a dose adjustment in this population.

Monitoring of erlotinib in pancreatic cancer patients during long-time administration and comparison to a physiologically based pharmacokinetic model

Purpose

In this study, a therapeutic drug monitoring (TDM) of erlotinib in pancreatic cancer patients was performed over 50 weeks to reveal possible alterations in erlotinib plasma concentrations. Additionally, a physiologically based pharmacokinetic (PBPK) model was created to assess such variations in silico.

Methods

Patients with advanced pancreatic cancer received a chemotherapeutic combination of 100 mg erlotinib q.d., 500–900 mg capecitabine b.d. and 5 mg/kg bevacizumab q.2wks. Samples were analyzed by HPLC and the results were compared to a PBPK model, built with the software GastroPlus™ and based on calculated and literature data.

Results

The erlotinib plasma concentrations did not show any accumulation, but displayed a high inter-patient variability over the whole investigated period. Trough plasma concentrations ranged from 0.04 to 1.22 µg/ml after day 1 and from 0.01 to 2.4 µg/ml in the long-term assessment. 7% of the patients showed concentrations below the necessary activity threshold of 0.5 µg/ml during the first week. The impact of some co-variates on the pharmacokinetic parameters C_max and AUC_0–24 were shown in a PBPK model, including food effects, changes in body weight, protein binding or liver function and the concomitant intake of gastric acid reducing agents (ARAs).

Conclusion

This study presents the approach of combining TDM and PBPK modeling for erlotinib, a drug with a high interaction potential. TDM is an important method to monitor drugs with increased inter-patient variability, additionally, the PBPK model contributed valuable insights to the interaction mechanisms involved, resulting in an effective combination from a PK perspective to ensure a safe treatment.

Clinical Bioavailability of the Novel BACE1 Inhibitor Lanabecestat (AZD3293): Assessment of Tablet Formulations Versus an Oral Solution and the Impact of Gastric pH on Pharmacokinetics

The relative bioavailability of lanabecestat administered as 2 tablet formulations versus an oral solution was investigated. This phase 1 single‐center, open‐label, randomized, 3‐period crossover study involved healthy male and nonfertile female subjects aged 18–55 years (NCT02039180). Subjects received a single 50‐mg lanabecestat dose as solution, tablet A, or tablet B on day 1 of each crossover period; 14 of 16 subjects completed the study. Relative bioavailability based on plasma lanabecestat AUC_0–∞ (area under the plasma drug concentration–time curve from zero to infinity) geometric mean ratio versus oral solution (primary variable) was: tablet A, 1.052 (90% confidence interval [CI], 1.001–1.106); tablet B, 1.040 (0.989–1.093). These 90%CIs for geometric mean ratios are within accepted standard bioequivalence boundaries for all other pharmacokinetic (PK) parameters for both lanabecestat and metabolite (AZ13569724). All 3 formulations had similar plasma lanabecestat concentration–time profiles. Six adverse events were reported by 6 subjects (37.5%, all mild). GastroPlus modeling predicted a negligible impact of gastric pH changes on systemic PK (up to pH 7.4). Both tablet formulations fall within standard accepted bioequivalence criteria versus the oral solution. A single 50‐mg lanabecestat dose was well tolerated as a solution or tablet formulation in this population.

Ribociclib Bioavailability Is Not Affected by Gastric pH Changes or Food Intake: In Silico and Clinical Evaluations

Ribociclib (KISQALI), a cyclin‐dependent kinase 4/6 inhibitor approved for the first‐line treatment of HR+/HER2– advanced breast cancer with an aromatase inhibitor, is administered with no restrictions on concomitant gastric pH‐elevating agents or food intake. The influence of proton pump inhibitors (PPIs) on ribociclib bioavailability was assessed using 1) biorelevant media solubility, 2) physiologically based pharmacokinetic (PBPK) modeling, 3) noncompartmental analysis (NCA) of clinical trial data, and 4) population PK (PopPK) analysis. This multipronged approach indicated no effect of gastric pH changes on ribociclib PK and served as a platform for supporting ribociclib labeling language, stating no impact of gastric pH‐altering agents on the absorption of ribociclib, without a dedicated drug–drug interaction trial. The bioequivalence of ribociclib exposure with or without a high‐fat meal was demonstrated in a clinical trial. Lack of restrictions on ribociclib dosing may facilitate better patient compliance and therefore clinical benefit.

Combining "Bottom-up" and "Top-down" Approaches to Assess the Impact of Food and Gastric pH on Pictilisib (GDC-0941) Pharmacokinetics

Pictilisib, a weakly basic compound, is an orally administered, potent, and selective pan-inhibitor of phosphatidylinositol 3-kinases for oncology indications. To investigate the significance of high-fat food and gastric pH on pictilisib pharmacokinetics (PK) and enable label recommendations, a dedicated clinical study was conducted in healthy volunteers, whereby both top-down (population PK, PopPK) and bottom-up (physiologically based PK, PBPK) approaches were applied to enhance confidence of recommendation and facilitate the clinical development through scenario simulations. The PopPK model identified food (for absorption rate constant (Ka )) and proton pump inhibitors (PPI, for relative bioavailability (Frel ) and Ka ) as significant covariates. Food and PPI also impacted the variability of Frel . The PBPK model accounted for the supersaturation tendency of pictilisib, and gastric emptying physiology successfully predicted the food and PPI effect on pictilisib absorption. Our research highlights the importance of applying both quantitative approaches to address critical drug development questions.

Physiologically Based Oral Absorption Modelling to Study Gut-Level Drug Interactions

Physiologically based oral absorption models are in silico tools primarily used to guide formulation development and project the clinical performance of formulation variants. This commentary briefly discusses additional oral absorption model applications, focusing on gut-level drug interactions. Gut-level drug interactions can involve drug degradation, metabolic enzymes, transporters, gastrointestinal motility modulators, acid-reducing agents, and food. The growth in publications reporting physiologically based oral absorption model utilization and successful pharmacokinetic prediction (e.g., after acid-reducing agents or food coadministration) indicate that oral absorption models have achieved a level of maturity within the industry particularly over the past 15 years. Provided appropriate data and model validation, oral absorption modeling/simulation may serve as a surrogate for clinical studies by providing both mechanistic and quantitative understanding of oral delivery considerations on pharmacokinetics.

Metabolites of alectinib in human: their identification and pharmacological activity

Two metabolites (M4 and M1b) in plasma and four metabolites (M4, M6, M1a and M1b) in faeces were detected through the human ADME study following a single oral administration of [¹⁴C]alectinib, a small-molecule anaplastic lymphoma kinase inhibitor, to healthy subjects. In the present study, M1a and M1b, which chemical structures had not been identified prior to the human ADME study, were identified as isomers of a carboxylate metabolite oxidatively cleaved at the morpholine ring. In faeces, M4 and M1b were the main metabolites, which shows that the biotransformation to M4 and M1b represents two main metabolic pathways for alectinib. In plasma, M4 was a major metabolite and M1b was a minor metabolite. The contribution to in vivo pharmacological activity of these circulating metabolites was assessed from their in vitro pharmacological activity and plasma protein binding. M4 had a similar cancer cell growth inhibitory activity and plasma protein binding to that of alectinib, suggesting its contribution to the antitumor activity of alectinib, whereas the pharmacological activity of M1b was insignificant.