Biliary Excretion-Mediated Food Effects and Prediction

Mechanistic Modeling of Empagliflozin: Predicting Pharmacokinetics, Urinary Glucose Excretion, and Investigating Compensatory Role of SGLT1 in Renal Glucose Reabsorption

The aim of this study was to use a combination of physiologically based pharmacokinetic (PBPK) modeling and urinary glucose excretion (UGE) modeling to predict the time profiles of pharmacokinetics (PK) and UGE for the sodium-glucose cotransporter 2 (SGLT2) inhibitor empagliflozin (EMP). Additionally, the study aims to explore the compensatory effect of SGLT1 in renal glucose reabsorption (RGR) when SGLT2 is inhibited. The PBPK-UGE model was developed using physicochemical and biochemical properties, renal physiological parameters, binding kinetics, glucose, and Na+ reabsorption kinetics by SGLT1/2. For area under the plasma concentration-time curve, maximum plasma concentration, and cumulative EMP excretion in urine, the predicted values fell within a range of 0.5-2.0 when compared to observed data. Additionally, the simulated UGE data also matched well with the clinical data, further validating the accuracy of the model. According to the simulations, SGLT1 and SGLT2 contributed approximately 13% and 87%, respectively, to RGR in the absence of EMP. However, in the presence of EMP at doses of 2.5 and 10 mg, the contribution of SGLT1 to RGR significantly increased to approximately 76%-82% and 89%-93%, respectively, in patients with type 2 diabetes mellitus. Furthermore, the model supported the understanding that the compensatory effect of SGLT1 is the underlying mechanism behind the moderate inhibition observed in total RGR. The PBPK-UGE model has the capability to accurately predict the PK and UGE time profiles in humans. Furthermore, it provides a comprehensive analysis of the specific contributions of SGLT1 and SGLT2 to RGR in the presence or absence of EMP.

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

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

Does Food Affect the Pharmacokinetics of Non-orally Delivered Drugs? A Review of Currently Available Evidence

The food effects for orally administered drugs have been widely investigated and reviewed. In contrast, our knowledge of food effects for non-orally administered drugs is scarce. In this review paper, we did a literature survey to collect clinical food effect data for non-orally administered drugs. Our survey retrieved 18 drugs, including thirteen intravenously (IV), two subcutaneously (SC), one intradermally (ID), one pulmonary, and one rectally administered drug. The food effect data show that food intake can increase the absorption of SC and ID administered peptides and proteins with MW < 30 kDa by 30-50%. On the other hand, food intake can increase the elimination of IV and inhaled drugs with moderate and high hepatic extraction and reduce drug exposure by up to 35%. The food effect knowledge can be used to mitigate potential efficacy and safety risks of non-orally administered drugs.

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

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

Meta-Analysis of Food Effect on Oral Absorption of Efflux Transporter Substrate Drugs: Does Delayed Gastric Emptying Influence Drug Transport Kinetics?

The oral route of drug administration is the most convenient method of drug delivery, but it is associated with variable bioavailability. Food is one of the major factors that affect oral drug absorption by influencing drug properties (e.g., solubility and dissolution rate) and physiological factors (e.g., metabolism and transport across the gastrointestinal tract). The aim of this work was to investigate the effect of food on the high-affinity intestinal efflux transporter substrate drugs. We hypothesized that transport efficiency is higher in the fed state as compared to the fasted state because of the lower intestinal lumen drug concentration due to prolonged gastric emptying time. A systematic analysis of reported clinical food-effect (FE) studies on 311 drugs was performed and the association of the efflux transport efficiency was investigated on the FE magnitude, i.e., changes in maximal plasma concentration and area under the plasma concentration-time profile curve for both solubility and permeability-limited drugs. In total, 124 and 88 drugs showed positive and negative FE, respectively, whereas 99 showed no FE. As expected, the solubility-limited drugs showed positive FE, but interestingly, drugs with a high potential for efflux transport, were associated with negative FE. Moreover, a high-fat diet was associated with a higher magnitude of negative FE for high-affinity efflux transporter substrates as compared to a low-fat diet. To account for changes in drug absorption after food intake, the prolonged gastric emptying time should be considered in the physiologically based pharmacokinetic (PBPK) modeling of orally absorbed efflux transporter substrate drugs.

Comparison of Canine and Human Physiological Factors: Understanding Interspecies Differences that Impact Drug Pharmacokinetics

This review is a summary of factors affecting the drug pharmacokinetics (PK) of dogs versus humans. Identifying these interspecies differences can facilitate canine-human PK extrapolations while providing mechanistic insights into species-specific drug in vivo behavior. Such a cross-cutting perspective can be particularly useful when developing therapeutics targeting diseases shared between the two species such as cancer, diabetes, cognitive dysfunction, and inflammatory bowel disease. Furthermore, recognizing these differences also supports a reverse PK extrapolations from humans to dogs. To appreciate the canine-human differences that can affect drug absorption, distribution, metabolism, and elimination, this review provides a comparison of the physiology, drug transporter/enzyme location, abundance, activity, and specificity between dogs and humans. Supplemental material provides an in-depth discussion of certain topics, offering additional critical points to consider. Based upon an assessment of available state-of-the-art information, data gaps were identified. The hope is that this manuscript will encourage the research needed to support an understanding of similarities and differences in human versus canine drug PK.