Physiologically Based Pharmacokinetic Modeling of Oral Absorption, pH, and Food Effect in Healthy Volunteers to Drive Alpelisib Formulation Selection

Single Dose of Phosphatidylinositol 3-Kinase Inhibitor Alpelisib Induces Insulin Resistance in Healthy Adults: A Randomized Feasibility Study

Our objective was to test a single dose of the phosphatidylinositol 3-kinase (PI3K) inhibitor alpelisib as a tool for acute modeling of insulin resistance in healthy volunteers. This single-center double-blind phase 1 clinical trial randomly assigned healthy adults to a single oral dose of 300 mg alpelisib (n = 5) or placebo (n = 6) at bedtime, followed by measurement of glucose, insulin, and C-peptide levels after an overnight fast and during a 3-h 75-g oral glucose tolerance test (OGTT). Fasting plasma glucose trended higher with alpelisib (mean ± SD 93 ± 11 mg/dL) versus placebo (84 ± 5 mg/dL); mean fasting serum insulin increased nearly fivefold (23 ± 12 vs. 5 ± 3 μU/mL, respectively), and HOMA of insulin resistance (IR) scores were 5.4 ± 3.1 for alpelisib and 1.1 ± 0.6 for placebo. During OGTT, incremental area under the curve (AUC) for insulin was more than fourfold greater with alpelisib (22 ± 15 mU/mL × min) than with placebo (5 ± 2 mU/mL × min); glucose AUC trended higher with alpelisib. Single-dose alpelisib was well tolerated and produced metabolic alterations consistent with acute induction of IR, validating its use for mechanistic study of insulin action in humans.

ARTICLE HIGHLIGHTS

Advancements in Virtual Bioequivalence: A Systematic Review of Computational Methods and Regulatory Perspectives in the Pharmaceutical Industry

BACKGROUND/OBJECTIVES

The rise of virtual bioequivalence studies has transformed the pharmaceutical landscape, enabling more efficient drug development processes. This systematic review aims to explore advancements in physiologically based pharmacokinetic (PBPK) modeling, its regulatory implications, and its role in achieving virtual bioequivalence, particularly for complex drug formulations.

METHODS

We conducted a systematic review of clinical trials using computational methods, particularly PBPK modeling, to carry out bioequivalence assessments. Eligibility criteria are emphasized during in silico modeling and pharmacokinetic simulations. Comprehensive literature searches were performed across databases such as PubMed, Scopus, and the Cochrane Library. A search strategy using key terms and Boolean operators ensured that extensive coverage was achieved. We adhered to the PRISMA guidelines in regard to the study selection, data extraction, and quality assessment, focusing on key characteristics, methodologies, outcomes, and regulatory perspectives from the FDA and EMA.

RESULTS

Our findings indicate that PBPK modeling significantly enhances the prediction of pharmacokinetic profiles, optimizing dosing regimens, while minimizing the need for extensive clinical trials. Regulatory agencies have recognized this utility, with the FDA and EMA developing frameworks to integrate in silico methods into drug evaluations. However, challenges such as study heterogeneity and publication bias may limit the generalizability of the results.

CONCLUSIONS

This review highlights the critical need for standardized protocols and robust regulatory guidelines to facilitate the integration of virtual bioequivalence methodologies into pharmaceutical practices. By embracing these advancements, the pharmaceutical industry can improve drug development efficiency and patient outcomes, paving the way for innovative therapeutic solutions. Continued research and adaptive regulatory frameworks will be essential in navigating this evolving field.

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Physiologically Based Biopharmaceutics Modeling for Gefapixant IR Formulation Development and Defining the Bioequivalence Dissolution Safe Space

Gefapixant is a weakly basic drug which has been formulated as an immediate release tablet for oral administration. A physiologically based biopharmaceutics model (PBBM) was developed based on gefapixant physicochemical properties and clinical pharmacokinetics to aid formulation selection, bioequivalence safe space assessment and dissolution specification settings. In vitro dissolution profiles of different free base and citrate salt formulations were used as an input to the model. The model was validated against the results of independent studies, which included a bioequivalence and a relative bioavailability study, as well as a human ADME study, all meeting acceptance criteria of prediction errors ≤ 20% for both Cmax and AUC.  PBBM was also applied to evaluate gastric pH-mediated drug-drug-interaction potential with co-administration of a proton pump inhibitor (PPI), omeprazole. Model results showed good agreement with clinical data in which omeprazole lowered gefapixant exposure for the free base formulation but did not significantly alter gefapixant pharmacokinetics for the citrate based commercial drug product. An extended virtual dissolution bioequivalence safe space was established.  Gefapixant drug product batches are anticipated to be bioequivalent with the clinical reference batch when their dissolution is > 80% in 60 minutes. PBBM established a wide dissolution bioequivalence space as part of assuring product quality.

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.

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.

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

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

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.

Dynamic Changes in Gastrointestinal Fluid Characteristics after Food Ingestion Are Important for Quantitatively Predicting the In Vivo Performance of Oral Solid Dosage Forms in Humans in the Fed State

The aim of this study was to develop a simulation model to predict the in vivo performance of solid oral dosage forms in humans in the fed state. We focused on investigating the effect of dynamic changes in gastrointestinal (GI) fluid characteristics in the fed state on the in vivo performance of solid dosage forms. We used six solid dosage forms containing weak base drugs as model formulations, two with positive food effects in humans, two with negative food effects, and two which are not affected by food ingestion. These model drug formulations were used to perform biorelevant dissolution tests in the stomach and small intestine under both prandial states. The in vitro properties of the drug products obtained from these tests were then coupled with in silico models (fasted or fed) to predict food effects in humans. We successfully incorporated the dynamic changes in GI fluid characteristics and their effects on the in vivo dissolution of drugs into the prediction model for the fed state. This newly designed physiologically based biopharmaceutics modeling approach provided the precise and quantitative prediction of food effects (i.e., changes in C_max and AUC after food ingestion) in humans while considering the dynamic changes in fluid characteristics in the fed state.

Pharmacokinetics and Pharmacodynamic of Alpelisib

Advanced breast cancers are frequently hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative. Some of them harbor a mutation in PIK3CA, a gene encoding the PI3K catalytic subunit α of phosphatidyl-inositol 3-kinase (PI3K), which confers resistance to hormone therapy. Alpelisib is the first oral selective p110 [Formula: see text] PI3K inhibitor approved by FDA and EMA, in association with fulvestrant, based on PFS improvement as compared to fulvestrant alone. The aim of this review is to summarize and critically review the key aspects of alpelisib pharmacokinetics (PK) and pharmacodynamics (PD). Preclinical data have shown that alpelisib IC₅₀ was 50 times lower for the α enzyme than for the β, δ and γ PI3K enzymes, leading to a decrease in intra-tumoral AKT phosphorylation. The PK properties of alpelisib are somehow favorable, with a rapid and important absorption, a limited CYP P450-mediated metabolism and a predominant biliary excretion, with a half-life of 17.5 ± 5.9 h. Only limited drug-drug interactions are expected and there is no need for dose adaptation in mild and moderate renal impaired and mild to severe hepatic impaired patients. Pharmacokinetic/pharmacodynamic relationships were evidenced during drug development for exposure/efficacy, but also exposure/safety. Main adverse events are hyperglycemia, rash, and diarrhea. The first, if not fully contra-indicated in (pre-)diabetic patients, warrants a close follow up when treatment is started and a potential dose reduction when needed. Because of its safety profile, alpelisib require stringent patient selection and close follow-up.

In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools

In the past decade, only a small number of papers have elaborated on the application of physiologically based pharmacokinetic (PBPK) modeling across different areas. In this review, an in-depth analysis of the distribution of PBPK modeling in relation to its application in various research topics and model validation was conducted by text mining tools. Orange 3.32.0, an open-source data mining program was used for text mining. PubMed was used for data retrieval, and the collected articles were analyzed by several widgets. A total of 2699 articles related to PBPK modeling met the predefined criteria. The number of publications per year has been rising steadily. Regarding the application areas, the results revealed that 26% of the publications described the use of PBPK modeling in early drug development, risk assessment and toxicity assessment, followed by absorption/formulation modeling (25%), prediction of drug-disease interactions (20%), drug-drug interactions (DDIs) (17%) and pediatric drug development (12%). Furthermore, the analysis showed that only 12% of the publications mentioned model validation, of which 51% referred to literature-based validation and 26% to experimentally validated models. The obtained results present a valuable review of the state-of-the-art regarding PBPK modeling applications in drug discovery and development and related fields.

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

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

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.

Fed or fasted state for oral therapies in breast cancer treatment? A comprehensive review of clinical practice recommendations

In the last decades several anti-cancer drugs have been developed to treat patients with breast cancer, many of them orally administered, with ongoing efforts to substitute parenteral drugs with oral therapy. The latter is attractive because of its convenience and ease of administration, finally improving quality of life. The drawback of oral administration is that exposure to the drug is affected by different factors and the high variability, combined with the relatively narrow therapeutic index of most of these agents, would predispose some individuals to risk for treatment inefficacy or increase toxicity. Among these factors, food plays a central role since it can influence the pharmacokinetic profile of several drugs. Consequently, health care providers and patients should be aware of possible interaction to optimize treatment. In this review a systematic evaluation of package inserts and literature have been performed to analyse the effect of fed or fasted state on pharmacokinetic of all oral drugs currently approved for breast cancer, offering clear recommendations for their use daily practice.

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.