Predicting the impact of physiological and biochemical processes on oral drug bioavailability

Pharmacokinetics of Intranasal Drugs, Still a Missed Opportunity?

The intranasal (IN) route of administration is important for topical drugs and drugs intended to act systemically. More recently, direct nose-to-brain input was considered to bypass the blood-brain barrier.Processes related to IN absorption and nose-to-brain distribution are complex and depend, sometimes in contrasting ways, on chemico-physical and structural parameters of the compounds, and on formulation options.Due to the intricacies of these processes and despite the large number of articles published on many different IN compounds, it appears that absorption after IN dosing is not yet fully understood. In particular, at variance of the understanding and modelling approaches that are available for predicting the pharmacokinetics (PK) following oral administration of xenobiotics, it appears that there is not a similar understanding of the chemico-physical and structural determinants influencing drug absorption and disposition of compounds after IN administration, which represents a missed opportunity for this research field. This is even more true regarding the understanding of the direct nose-to-brain input. Due to this, IN administrations may represent an interesting and open research field for scientists aiming to develop PK property predictions tools, mechanistic PK models describing rate and extent of IN absorption, and translational tools to anticipate the clinical PK following IN dosing based on in vitro and in vivo non clinical experiments.This review intends to provide: i) some basic knowledge related to the physiology of PK after IN dosing, ii) a non-exhaustive list of preclinical and clinical examples related to compounds explored for the potential nose-to-blood and nose-to-brain passage, and iii) the identification of some areas requiring improvements, the understanding of which may facilitate the development of IN drug candidates.

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.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Physiologically Based Pharmacokinetic (PBPK) Model Predictions of Disease Mediated Changes in Drug Disposition in Patients with Nonalcoholic Fatty Liver Disease (NAFLD)

PURPOSE

This study was designed to verify a virtual population representing patients with nonalcoholic fatty liver disease (NAFLD) to support the implementation of a physiologically based pharmacokinetic (PBPK) modeling approach for prediction of disease-related changes in drug pharmacokinetics.

METHODS

A virtual NAFLD patient population was developed in GastroPlus (v.9.8.2) by accounting for pathophysiological changes associated with the disease and proteomics-informed alterations in the abundance of metabolizing enzymes and transporters pertinent to drug disposition. The NAFLD population model was verified using exemplar drugs where elimination is influenced predominantly by cytochrome P450 (CYP) enzymes (chlorzoxazone, caffeine, midazolam, pioglitazone) or by transporters (rosuvastatin, 11C-metformin, morphine and the glucuronide metabolite of morphine).

RESULTS

PBPK model predictions of plasma concentrations of all the selected drugs and hepatic radioactivity levels of 11C-metformin were consistent with the clinically-observed data. Importantly, the PBPK simulations using the virtual NAFLD population model provided reliable estimates of the extent of changes in key pharmacokinetic parameters for the exemplar drugs, with mean predicted ratios (NAFLD patients divided by healthy individuals) within 0.80- to 1.25-fold of the clinically-reported values, except for midazolam (prediction-fold difference of 0.72).

CONCLUSION

A virtual NAFLD population model within the PBPK framework was successfully developed with good predictive capability of estimating disease-related changes in drug pharmacokinetics. This supports the use of a PBPK modeling approach for prediction of the pharmacokinetics of new investigational or repurposed drugs in patients with NAFLD and may help inform dose adjustments for drugs commonly used to treat comorbidities in this patient population.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

Bridging population pharmacokinetic and semimechanistic absorption modeling of APX3330

APX3330 ((2E)-2-[(4,5-dimethoxy-2-methyl-3,6-dioxo-1,4-cyclohexadien-1-yl)methylene]-undecanoic acid), a selective inhibitor of APE1/Ref-1, has been investigated in treatment of hepatitis, cancer, diabetic retinopathy, and macular edema. APX3330 is administered orally as a quinone but is rapidly converted to the hydroquinone form. This study describes the pharmacokinetics of APX3330 and explores effect of food on absorption. Total plasma quinone concentrations of APX3330 were obtained following oral administration from studies in healthy Japanese male subjects (single dose-escalation; multiple-dose; food-effect) and patients with cancer patients. Nonlinear mixed effects modeling was performed using Monolix to estimate pharmacokinetic parameters and assess covariate effects. To further evaluate the effect of food on absorption, a semi-physiologic pharmacokinetic model was developed in Gastroplus to delineate effects of food on dissolution and absorption. A two-compartment, first order absorption model with lag time best described plasma concentration-time profiles from 49 healthy Japanese males. Weight was positively correlated with apparent clearance (CL/F) and volume. Administration with food led to an 80% higher lag time. CL/F was 41% higher in the cancer population. The semi-physiologic model indicates a switch from dissolution-rate control of absorption in the fasted-state to gastric emptying rate determining absorption rate in the fed-state. Oral clearance of APX3330 is higher in patients with cancer than healthy Japanese males, possibly due to reduced serum albumin in patients with cancer. Delayed APX3330 absorption with food may be related to higher conversion to the more soluble but less permeable hydroquinone form in the gastrointestinal tract. Future work should address pharmacokinetic differences between APX3330 quinone and hydroquinone forms.

The Complexity of Bariatric Patient's Pharmacotherapy: Sildenafil Biopharmaceutics and Pharmacokinetics before vs. after Gastric Sleeve/Bypass

Postbariatric altered gastrointestinal (GI) anatomy/physiology may significantly harm oral drug absorption and overall bioavailability. In this work, sildenafil, the first phosphodiesterase-5 (PDE5) inhibitor, was investigated for impaired postbariatric solubility/dissolution and absorption; this research question is of particular relevance since erectile dysfunction (ED) is associated with higher body mass index (BMI). Sildenafil solubility was determined both in vitro and ex vivo, using pre- vs. postsurgery gastric contents aspirated from patients. Dissolution tests were done in conditions mimicking the stomach before surgery, after sleeve gastrectomy (post-SG, pH 5), and after one anastomosis gastric bypass (post-OAGB, pH 7). Finally, these data were included in physiologically based pharmacokinetic (PBPK) modelling (GastroPlus®) to simulate sildenafil PK before vs. after surgery. pH-dependent solubility was demonstrated with low solubility (0.3 mg/mL) at pH 7 vs. high solubility at pH 1-5, which was also confirmed ex vivo with much lower solubility values in postbariatric gastric samples. Hampered dissolution of all sildenafil doses was obtained under post-OAGB conditions compared with complete (100%) dissolution under both presurgery and post-SG conditions. PBPK simulations revealed delayed sildenafil absorption in postbariatric patients (increased tmax) and reduced Cmax, especially in post-OAGB patients, relative to a presurgery state. Hence, the effect of bariatric surgery on sildenafil PK is unpredictable and may depend on the specific bariatric procedure. This mechanistically based analysis suggests a potentially undesirable delayed onset of action of sildenafil following gastric bypass surgery.

Approaches to Account for Colon Absorption in Physiologically Based Biopharmaceutics Modeling of Extended-Release Drug Products

The rate and extent of colon absorption are important determinants of the in vivo performance of extended-release (ER) drug products. The ability to appropriately predict this at different stages of development using mechanistic physiologically based biopharmaceutic modeling (PBBM) is highly desirable. This investigation aimed to evaluate the prediction performance of three different approaches to account for colon absorption in predictions of the in vivo performance of ER drug product variants with different in vitro release profiles. This was done by mechanistic predictions of the absorption and plasma exposure of the ER drug products using GastroPlus and GI-Sim for five drugs with different degrees of colon absorption limitations in humans. Colon absorption was accounted for in the predictions using three different approaches: (1) by an a priori approach using the default colon models, (2) by fitting the colon absorption scaling factors to the observed plasma concentration-time profiles after direct administration to the colon in humans, or (3) from the ER drug product variant with the slowest in vitro release profile. The prediction performance was evaluated based on the percentage prediction error and the average absolute prediction error (AAPE). Two levels of acceptance criteria corresponding to highly accurate (AAPE ≤ 20%) and accurate (AAPE 20-50%) predictions were defined prior to the evaluation. For the a priori approach, the relative bioavailability (Frel), AUC0-t, and Cmax of the ER drug product variants for the low to medium colon absorption limitation risk drugs was accurately predicted with an AAPE range of 11-53 and 8-59% for GastroPlus and GI-Sim, respectively. However, the prediction performance was poor for the high colon absorption limitation risk drugs. Moreover, accounting for the human regional colon absorption data in the models did not improve the prediction performance. In contrast, using the colon absorption scaling factors derived from the slowest ER variant significantly improved the prediction performance regardless of colon absorption limitation, with a majority of the predictions meeting the high accuracy criteria. For the slowest ER approach, the AAPE ranges were 5-24 and 5-32% for GastroPlus and GI-Sim, respectively, excluding the low permeability drug. In conclusion, the a priori PBBM can be used during candidate selection and early product design to predict the in vivo performance of ER drug products for low to medium colon absorption limitation risk drugs with sufficient accuracy. The results also indicate a limited value in performing human regional absorption studies in which the drug is administered to the colon as a bolus to support PBBM development for ER drug products. Instead, by performing an early streamlined relative bioavailability study with the slowest relevant ER in vitro release profile, a highly accurate PBBM suitable for ER predictions for commercial and regulatory applications can be developed, except for permeability-limited drugs.

Physiological Dynamics in the Upper Gastrointestinal Tract and the Development of Gastrointestinal Absorption Models for the Immediate-Release Oral Dosage Forms in Healthy Adult Human

This review is a revisit of various oral drug absorption models developed in the past decades, focusing on how to incorporate the physiological dynamics in the upper gastrointestinal (GI) tract. For immediate-release oral drugs, GI absorption is a critical input of drug exposure and subsequent human body response, yet difficult to model largely due to the complex GI environment. One of the biggest hurdles lies at capturing the high within-subject variability (WSV) of bioavailability measures, which can be mechanistically explained by the GI physiological dynamics. A thorough summary of how GI dynamics is handled in the absorption models would promote the development of mechanism-based oral drug absorption models, aid in the design of clinical studies regarding dosing regimens and bioequivalence studies based on WSV, and advance the decision-making on formulation selection.

Oral delivery of a host-directed antiviral, niclosamide, as a cholate-coated nanoformulation

Potentially significant drug candidates often face elimination from consideration due to the lack of an effective method for systemic delivery. The poor solubility of these candidates has posed a major obstacle for their development as oral pills or injectables. Niclosamide, a host-directed antiviral, is a good example. In this study, a nanoformulation technology that allows for the non-covalent formulation of niclosamide with cholic acids was developed. This formulation enables efficient systemic delivery through endocytosis and enterohepatic circulation of bile-acid-coated nanoparticles. The oral bioavailability of niclosamide-delivery nanoparticles (NDNs) was significantly enhanced to 38.3%, representing an eight-fold increase compared with pure niclosamide. Consequently, the plasma concentration of niclosamide for the NDN formulation reached 1179.6 ng/mL, which is 11 times higher than the therapeutic plasma level. This substantial increase in plasma level contributed to the complete resolution of clinical symptoms in animals infected with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This nanoformulation not only provides an orally deliverable antiviral drug for SARS-CoV-2 with improved pharmaceutical bioavailability, but also offers a solution to the systemic delivery challenges faced by potentially significant drug candidates.

Physiologically based pharmacokinetic modeling to characterize enterohepatic recirculation and predict food effect on the pharmacokinetics of hyzetimibe

BACKGROUND AND OBJECTIVE

Hyzetimibe is a cholesterol absorption inhibitor indicated for the treatment of hypercholesterolemia. This study aims to describe the multiple-peak pharmacokinetics (PK) of hyzetimibe and its active metabolite M1 through physiologically-based pharmacokinetic (PBPK) modeling, and to compare the model predictions of a virtual food effect study with the results of a clinical food effect study.

METHODS

The plasma concentration data used for PBPK modeling were obtained from a single-dose, two-period crossover bioequivalence study in the fasted state. Advanced Compartmental Absorption and Transit model was used for absorption. Enterohepatic recirculation process was modeled by changing the gut physiological state from fasted to fed at meal time. Based on the established PBPK models, a virtual food effect study was simulated. A clinical food effect study was used for model external validation.

RESULTS

PK profiles of hyzetimibe and M1 under fasting condition could be well described by the PBPK model, and the errors of Cmax, AUC0-∞, and AUC0-t were within the two-fold range. Simulated geometric mean ratios (GMRs, fed/fasted) showed that a high-fat breakfast slightly affected the PK of hyzetimibe, expressed as increased Cmax of hyzetimibe (130.6%). Simulated GMRs and 90% confidence intervals of AUC were within the preset bioequivalent range. The results of the simulated virtual food effect trial were consistent with those of the clinical food effect trial.

CONCLUSIONS

The established PBPK model could describe the concentration-time profiles of hyzetimibe and M1 well with good prediction performance. A fully mechanistic model of enterohepatic recirculation warrants further investigation.

Adapting physiologically-based pharmacokinetic models for machine learning applications

Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine learning (ML) pipelines could offer significant benefits in improving the accuracy and scope of drug screening and evaluation procedures. Here, we describe the development and testing of a self-contained machine learning module capable of faithfully recapitulating summary pharmacokinetic (PK) parameters produced by a full PBPK model, given a set of input drug-specific and regimen-specific information. Because of its widespread use in characterizing the disposition of orally administered drugs, the PBPK model chosen to demonstrate the methodology was an open-source implementation of a state-of-the-art compartmental and transit model called OpenCAT. The model was tested for drug formulations spanning a large range of solubility and absorption characteristics, and was evaluated for concordance against predictions of OpenCAT and relevant experimental data. In general, the values predicted by the ML models were within 20% of those of the PBPK model across the range of drug and formulation properties. However, summary PK parameter predictions from both the ML model and full PBPK model were occasionally poor with respect to those derived from experiments, suggesting deficiencies in the underlying PBPK model.

Development and In Vitro-In Vivo Correlation Evaluation of IMM-H014 Extended-Release Tablets for the Treatment of Fatty Liver Disease

This study aimed to develop extended-release tablets containing 25 mg IMM-H014, an original drug formulated by a direct powder pressing method based on pharmaceutical-grade hydrophilic matrix polymers such as hydroxypropyl methylcellulose, to establish an in vitro-in vivo correlation (IVIVC) to predict bioavailability. The tablets' mechanical properties and in vitro and in vivo performance were studied. The formulation was optimized using a single-factor experiment and the reproducibility was confirmed. The in vitro dissolution profiles of the tablet were determined in five dissolution media, in which the drug released from the hydrophilic tablets followed the Ritger-Peppas model kinetics in 0.01 N HCl medium for the first 2 h, and in phosphate-buffered saline medium (pH 7.5) for a further 24 h. Accelerated stability studies (40 °C, 75% relative humidity) proved that the optimal formulation was stable for 6 months. The in vivo pharmacokinetics study in beagle dogs showed that compared to the IMM-H014 immediate release preparation, the maximum plasma concentration of the extended-release (ER) preparation was significantly decreased, while the maximum time to peak and mean residence time were significantly prolonged. The relative bioavailability was 97.9% based on the area under curve, indicating that the optimal formulation has an obvious ER profile, and a good IVIVC was established, which could be used to predict in vivo pharmacokinetics based on the formulation composition.

In Silico Studies of Novel Vemurafenib Derivatives as BRAF Kinase Inhibitors

BRAF inhibitors have improved the treatment of advanced or metastatic melanoma in patients that harbor a BRAF^T1799A mutation. Because of new insights into the role of aberrant glycosylation in drug resistance, we designed and studied three novel vemurafenib derivatives possessing pentose-associated aliphatic ligands-methyl-, ethyl-, and isopropyl-ketopentose moieties-as potent BRAF_V600E kinase inhibitors. The geometries of these derivatives were optimized using the density functional theory method. Molecular dynamic simulations were performed to find interactions between the ligands and BRAF_V600E kinase. Virtual screening was performed to assess the fate of derivatives and their systemic toxicity, genotoxicity, and carcinogenicity. The computational mapping of the studied ligand-BRAF_V600E complexes indicated that the central pyrrole and pyridine rings of derivatives were located within the hydrophobic ATP-binding site of the BRAF_V600E protein kinase, while the pentose ring and alkyl chains were mainly included in hydrogen bonding interactions. The isopropyl-ketopentose derivative was found to bind the BRAF_V600E oncoprotein with more favorable energy interaction than vemurafenib. ADME-TOX in silico studies showed that the derivatives possessed some desirable pharmacokinetic and toxicologic properties. The present results open a new avenue to study the carbohydrate derivatives of vemurafenib as potent BRAF_V600E kinase inhibitors to treat melanoma.

Oral nanoparticulate drug delivery systems for the treatment of intestinal bowel disease and colorectal cancer

INTRODUCTION

The most popular method for delivering drugs locally and systemically is oral. However, the gastrointestinal tract's severe physiological (mucosal and enzymatic barrier) and physicochemical (pH) environment places restrictions on the oral drug delivery system's bioavailability and targeted design.

AREAS COVERED

Various nanoparticulate drug delivery systems (NPDDSs) based on lipids or polymers, such as liposomes, solid lipid nanoparticles, polymeric micelles, nanospheres, and nanocapsules and their application in successful treatment of serious diseases such as intestinal bowel disease and colorectal cancer (CRC). These systems can ensure advantages over conventional systems liked improved bioavailability, prolonged residence time, and enhanced solubility of poorly soluble drugs. Moreover, the nature of these NPDDSs led to numerous breakthroughs in bioavailability, active and passive targeting, controlled release, and cost-efficient production on an industrial scale in recent years.

EXPERT OPINION

An expert opinion on orally administrable lipid and polymer based NPDDS, the physiological barriers and their use in the treatment of intestinal bowel disease and CRC is provided within this review.

Use of Physiologically Based Pharmacokinetic Modeling for Hepatically Cleared Drugs in Pregnancy: Regulatory Perspective

Physiologically based pharmacokinetic modeling could be used to predict changes in exposure during pregnancy and possibly inform medicine use in pregnancy in situations in which there is currently limited or no available clinical PK data. The Medicines and Healthcare Product Regulatory Agency has been evaluating the available models for a number of medicines cleared by hepatic clearance mechanisms. Models were evaluated for metoprolol, tacrolimus, clindamycin, ondansetron, phenytoin, caffeine, fluoxetine, clozapine, carbamazepine, metronidazole, and paracetamol. The hepatic metabolism through cytochrome P450 (CYP) contributes significantly to the elimination of these drugs, and available knowledge of CYP changes during pregnancy has been implemented in the existing pregnancy physiology models. In general, models were able to capture trends in exposure changes in pregnancy to some extent, but the magnitude of pharmacokinetic change for these hepatically cleared drugs was not captured in each case, nor were models always able to capture overall exposure in the populations. A thorough evaluation was hampered by the lack of clinical data for drugs cleared by a specific clearance pathway. The limited clinical data, as well as complex elimination pathways involving CYPs, uridine 5'-diphospho-glucuronosyltransferase and active transporter for many drugs, currently limit the confidence in the prospective use of the models. Pregnancy-related changes in uridine 5'-diphospho-glucuronosyltransferase and transport functions are emerging, and incorporation of such changes in current physiologically based pharmacokinetic modeling software is in progress. Filling this gap is expected to further enhance predictive performance of models and increase the confidence in predicting PK changes in pregnant women for hepatically cleared drugs.

Physiologically Based Biopharmaceutics Modeling of regional and colon absorption in humans

Colon absorption is a key determinant for successful development of extended release and colon targeted drug products. This is the first systematic evaluation of the ability to predict in vivo regional differences in absorption and the extent of colon absorption in humans using mechanistic physiologically based biopharmaceutics modeling (PBBM). A new dataset, consisting of 19 drugs with a wide range of biopharmaceutics properties and extent of colon absorption in humans, was established. Mechanistic predictions of the extent of absorption and plasma exposure after oral, or jejunal and direct colon administration were performed in GastroPlus and GI-Sim using an a priori approach. Two new colon models developed in GI-Sim, were also evaluated to assess if the prediction performance could be improved. Both GastroPlus and GI-Sim met the pre-defined criteria for accurate predictions of regional and colon absorption for high permeability drugs irrespective of formulation type, while the prediction performance was poor for low permeability drugs. For solutions, the two new GI-Sim colon models improved the colon absorption prediction performance for the low permeability drugs while maintaining the accurate prediction performance for the high permeability drugs. In contrast, the prediction performance decreased for non-solutions using the two new colon models. In conclusion, PBBM can be used with sufficient accuracy to predict regional and colon absorption in humans for high permeability drugs in candidate selection as well as early design and development of extended release or colon targeted drug products. The prediction performance of the current models needs to be improved to allow high accuracy predictions for commercial drug product applications including highly accurate predictions of the entire plasma concentration-time profiles as well as for low permeability drugs.

Significance of Basal Membrane Permeability of Epithelial Cells in Predicting Intestinal Drug Absorption

Drug absorption from the gastrointestinal tract is often restricted by efflux transport by P-glycoprotein (P-gp) and metabolism by CYP3A4. Both localize in the epithelial cells, and thus, their activities are directly affected by the intracellular drug concentration, which should be regulated by the ratio of permeability between apical (A) and basal (B) membranes. In this study, using Caco-2 cells with forced expression of CYP3A4, we assessed the transcellular permeation of A-to-B and B-to-A directions and the efflux from the preloaded cells to both sides of 12 representative P-gp or CYP3A4 substrate drugs and obtained the parameters for permeabilities, transport, metabolism, and unbound fraction in the enterocytes (f_ent) using simultaneous and dynamic model analysis. The membrane permeability ratios for B to A (R_BA) and f_ent varied by 8.8-fold and by more than 3000-fold, respectively, among the drugs. The R_BA values for digoxin, repaglinide, fexofenadine, and atorvastatin were greater than 1.0 (3.44, 2.39, 2.27, and 1.90, respectively) in the presence of a P-gp inhibitor, thus suggesting the potential involvement of transporters in the B membrane. The Michaelis constant for quinidine for P-gp transport was 0.077 µM for the intracellular unbound concentration. These parameters were used to predict overall intestinal availability (F_AF_G) by applying an intestinal pharmacokinetic model, advanced translocation model (ATOM), in which permeability of A and B membranes accounted separately. The model predicted changes in the absorption location for P-gp substrates according to its inhibition, and F_AF_G values of 10 of 12 drugs, including quinidine at varying doses, were explained appropriately. SIGNIFICANCE STATEMENT: Pharmacokinetics has improved predictability by identifying the molecular entities of metabolism and transport and by using mathematical models to appropriately describe drug concentrations at the locations where they act. However, analyses of intestinal absorption so far have not been able to accurately consider the concentrations in the epithelial cells where P-glycoprotein and CYP3A4 exert effects. In this study, the limitation was removed by measuring the apical and basal membrane permeability separately and then analyzing these values using new appropriate models.

Experimental and computational models to investigate intestinal drug permeability and metabolism

Oral administration is the preferred route for drug administration that leads to better therapy compliance. The intestine plays a key role in the absorption and metabolism of oral drugs, therefore, new intestinal models are being continuously proposed, which contribute to the study of intestinal physiology, drug screening, drug side effects, and drug-drug interactions.Advances in pharmaceutical processes have produced more drug formulations, causing challenges for intestinal models. To adapt to the rapid evolution of pharmaceuticals, more intestinal models have been created. However, because of the complexity of the intestine, few models can take all aspects of the intestine into account, and some functions must be sacrificed to investigate other areas. Therefore, investigators need to choose appropriate models according to the experimental stage and other requirements to obtain the desired results.To help researchers achieve this goal, this review summarised the advantages and disadvantages of current commonly used intestinal models and discusses possible future directions, providing a better understanding of intestinal models.

Bioequivalence study of ipratropium bromide inhalation aerosol using PBPK modelling

Aims

Systemic pharmacokinetic (PK) studies can reflect the overall exposure of orally inhaled drug Products (OIDPs) in the blood after inhalation into the lung and can be used to evaluate the bioequivalence of test and reference products. The aim of this article is: (1) to study the PK characteristics and bioequivalence of ipratropium bromide (IB) inhalation aerosol, reference and test products in healthy Chinese subjects; (2) to establish a physiologically based pharmacokinetic (PBPK) model and verify the accuracy of the model in predicting bioequivalence; (3) attempt to use the model to predict the regional distribution of particles in the lung after inhalation, and discuss the effect of gastrointestinal drug absorption of IB on systemic exposure.

Methods

The study involved two clinical studies. Clinical study-1 (registration number: CTR20201284) was used with non-clinical data to construct and validate a PBPK model in the B²O simulator, a web-based virtual drug development platform. This model assessed different test and reference products' bioequivalence. Results were compared to a second clinical study (Clinical study-2: registration number CTR20202291). The particles' regional distribution in the lung and the gastrointestinal absorption effect on systemic exposure were discussed based on the simulation results.

Results

The established PBPK model successfully simulated the in vivo PK characteristics of IB inhalation aerosol, with r ² close to 1. Gastrointestinal absorption had a negligible effect on systemic exposure. Particles accumulated in the alveolar area were cleared within an hour, followed by particles in the bronchioles and bronchi.

Conclusion

This model provided a reliable method for exploring the correlation between in vitro and in vivo PK studies of IB inhalation aerosols. According to the simulation results, the test and reference products were bioequivalent.

Hyaluronic acid-coated gold nanoparticles as a controlled drug delivery system for poorly water-soluble drugs

Hyaluronic acid (HA) is a natural linear polysaccharide which has been widely used in cosmetics and pharmaceuticals including drug delivery systems because of its excellent biocompatibility. In this study, we investigated the one-pot synthesis of HA-coated gold nanoparticles (AuNP-HA) as a drug delivery carrier. The HAs with different molecular weights were produced by e-beam irradiation and employed as coating materials for AuNPs. Sulfasalazine (SSZ), a poorly water-soluble drug, was used to demonstrate the efficiency of drug delivery and the controlled release behaviour of the AuNP-HA. As the molecular weight of the HA decreased, the drug encapsulation efficiency of the SSZ increased up to 94%, while drug loading capacity of the SSZ was maintained at the level of about 70%. The prepared AuNP-HA-SSZ exhibited slow release of the SSZ over a short time and excellent sensitivity to different pHs and physiological conditions. The SSZ release rate was the lowest in simulated gastric conditions and the highest in simulated intestinal conditions. In this case, the AuNP-HA protects the SSZ from release under the acidic pH conditions in the stomach; on the other hand, the drug release was facilitated in the basic environment of the small intestine and colon. The SSZ was released under simulated intestinal conditions through anomalous drug transport and followed the Korsmeyer-Peppas model. Therefore, this study suggests that AuNP-HA is a promising orally-administered and intestine-targeted drug delivery system with controlled release characteristics.

The Rat Continuous Intestine Model Predicts the Impact of Particle Size and Transporters on the Oral Absorption of Glyburide

Oral drug absorption is known to be impacted by the physicochemical properties of drugs, properties of oral formulations, and physiological characteristics of the intestine. The goal of the present study was to develop a mathematical model to predict the impact of particle size, feeding time, and intestinal transporter activity on oral absorption. A previously published rat continuous intestine absorption model was extended for solid drug absorption. The impact of active pharmaceutical ingredient particle size was evaluated with glyburide (GLY) as a model drug. Two particle size suspensions of glyburide were prepared with average particle sizes of 42.7 and 4.1 μm. Each suspension was dosed as a single oral gavage to male Sprague Dawley rats, and concentration-time (C-t) profiles of glyburide were measured with liquid chromatography coupled with tandem mass spectrometry. A continuous rat intestine absorption model was extended to include drug dissolution and was used to predict the absorption kinetics of GLY depending on particle size. Additional literature datasets of rat GLY formulations with particle sizes ranging from 0.25 to 4.0 μm were used for model predictions. The model predicted reasonably well the absorption profiles of GLY based on varying particle size and varying feeding time. The model predicted inhibition of intestinal uptake or efflux transporters depending on the datasets. The three datasets used formulations with different excipients, which may impact the transporter activity. Model simulations indicated that the model provides a facile framework to predict the impact of transporter inhibition on drug C-t profiles. Model simulations can also be conducted to evaluate the impact of an altered intestinal lumen environment. In conclusion, the rat continuous intestine absorption model may provide a useful tool to predict the impact of varying drug formulations on rat oral absorption profiles.

Prediction of Drug Synergism between Peptides and Antineoplastic Drugs Paclitaxel, 5-Fluorouracil, and Doxorubicin Using In Silico Approaches

Chemotherapy is the main treatment for most early-stage cancers; nevertheless, its efficacy is usually limited by drug resistance, toxicity, and tumor heterogeneity. Cell-penetrating peptides (CPPs) are small peptide sequences that can be used to increase the delivery rate of chemotherapeutic drugs to the tumor site, therefore contributing to overcoming these problems and enhancing the efficacy of chemotherapy. The drug combination is another promising strategy to overcome the aforementioned problems since the combined drugs can synergize through interconnected biological processes and target different pathways simultaneously. Here, we hypothesized that different peptides (P1-P4) could be used to enhance the delivery of chemotherapeutic agents into three different cancer cells (HT-29, MCF-7, and PC-3). In silico studies were performed to simulate the pharmacokinetic (PK) parameters of each peptide and antineoplastic agent to help predict synergistic interactions in vitro. These simulations predicted peptides P2-P4 to have higher bioavailability and lower T_max, as well as the chemotherapeutic agent 5-fluorouracil (5-FU) to have enhanced permeability properties over other antineoplastic agents, with P3 having prominent accumulation in the colon. In vitro studies were then performed to evaluate the combination of each peptide with the chemotherapeutic agents as well as to assess the nature of drug interactions through the quantification of the Combination Index (CI). Our findings in MCF-7 and PC-3 cancer cells demonstrated that the combination of these peptides with paclitaxel (PTX) and doxorubicin (DOXO), respectively, is not advantageous over a single treatment with the chemotherapeutic agent. In the case of HT-29 colorectal cancer cells, the combination of P2-P4 with 5-FU resulted in synergistic cytotoxic effects, as predicted by the in silico simulations. Taken together, these findings demonstrate that these CPP6-conjugates can be used as adjuvant agents to increase the delivery of 5-FU into HT-29 colorectal cancer cells. Moreover, these results support the use of in silico approaches for the prediction of the interaction between drugs in combination therapy for cancer.

Predicting the correct dose in children: Role of computational Pediatric Physiological-based pharmacokinetics modeling tools

The pharmacokinetics (PKs) and safety of medications in particular groups can be predicted using the physiologically-based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P-PBPK) modeling software in dose prediction over the past 6 years and briefly introduces the process of general P-PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients.

Determination of human FaFg of polyphenols using allometric scaling

Certain polyphenols exhibit low permeability; precise prediction of their intestinal absorption is important for understanding internal exposure in humans. Intestinal availability, which represents the fraction of administered compounds that reach the portal blood (F_aF_g), is calculated by dividing bioavailability (F) by hepatic availability (F_h), and F is obtained from pharmacokinetic data from both intravenous (i.v.) and oral (p.o.) administration. However, human F_aF_g of polyphenols is hardly reported, as human i.v. data are extremely scarce. In this study, we developed an estimation method for F_aF_g of polyphenols in humans based on the extrapolation of rat clearance using allometric scaling (allometric scaling-based F_aF_g calculation method, AS- F_aF_gCM). First, for quercetin, for which human i.v. data have been reported, we compared the F_aF_g obtained by AS-F_aF_gCM with the traditional approach using human i.v. and p.o. data. Less than two-fold difference in FaFg values was observed between the two approaches. Next, we obtained F_aF_g of structurally diverse polyphenols (genistein, baicalein, resveratrol, and epicatechin) using AS-F_aF_gCM, demonstrating that all of them were poorly absorbable. Furthermore, to utilize the pharmacokinetic data of the total concentration, including aglycones and metabolites, we modified the AS-F_aF_gCM to focus on their excretion. The F_aF_g value of naringenin was obtained using modified AS-F_aF_gCM and was nearly equal to that of baicalein, a structural isomer of naringenin. This study provides quantitative information on the intestinal absorption of polyphenols using comprehensive estimation methods.

Investigating the uncertainty of prediction accuracy for the application of physiologically based pharmacokinetic models to animal-free risk assessment of cosmetic ingredients

Physiologically based pharmacokinetic (PBPK) models are considered useful tools in animal-free risk assessment. To utilize PBPK models for risk assessment, it is necessary to compare their reliability with in vivo data. However, obtaining in vivo pharmacokinetics data for cosmetic ingredients is difficult, complicating the utilization of PBPK models for risk assessment. In this study, to utilize PBPK models for risk assessment without accuracy evaluation, we proposed a novel concept-the modeling uncertainty factor (MUF). By calculating the prediction accuracy for 150 compounds, we established that using in vitro data for metabolism-related parameters and limiting the applicability domain increase the prediction accuracy of a PBPK model. Based on the 97.5th percentile of prediction accuracy, MUF was defined at 10 for the area under the plasma concentration curve and 6 for C_max. A case study on animal-free risk assessment was conducted for bisphenol A using these MUFs. As this study was conducted mainly on pharmaceuticals, further investigation using cosmetic ingredients is pivotal. However, since internal exposure is essential in realizing animal-free risk assessment, our concept will serve as a useful tool to predict plasma concentrations without using in vivo data.

The Use of Pregnancy Physiologically Based Pharmacokinetic Modeling for Renally Cleared Drugs

Physiologically based pharmacokinetic modeling (PBPK) could be used to predict changes in exposure during pregnancy and possibly inform medicine use in pregnancy in situations where there are currently no available clinical data. The Medicines and Healthcare Product Regulatory Agency has been evaluating the available models for a number of medicines cleared by the kidney. Models were evaluated for ceftazidime, cefuroxime, metformin, oseltamivir, and amoxicillin. Because the passive renal process contributes significantly to the renal elimination of these drugs and changes of the process during pregnancy have been implemented in existing pregnancy physiology models, simulations using these models can reasonably describe the pharmacokinetics of ceftazidime changes during pregnancy and appears to generally capture the changes in the other medicines; however, there are insufficient data on drugs solely passively cleared to fully qualify the models. In addition, in many cases, active transport processes are involved in a drug's renal clearance. Knowledge of changes in renal transport functions during pregnancy is emerging, and incorporation of such changes in current physiologically based pharmacokinetic modeling software is a work in progress. Filling this gap is expected to further enhance predictive performance of the models and increase the confidence in predicting pharmacokinetic changes in pregnant women for other renally cleared drugs.

Antiallergic Treatment of Bariatric Patients: Potentially Hampered Solubility/Dissolution and Bioavailability of Loratadine, but Not Desloratadine, Post-Bariatric Surgery

Gastrointestinal anatomical/physiological changes after bariatric surgery influence variables affecting the fate of drugs after ingestion, and medication management of these patients requires a thorough and complex mechanistic analysis. The aim of this research was to study whether loratadine/desloratadine antiallergic treatment of bariatric patients is at risk of being ineffective due to impaired solubility/dissolution. The pH-dependent solubility of loratadine/desloratadine was studied in vitro, as well as ex vivo, in gastric content aspirated from patients before versus after bariatric surgery. Then, a biorelevant dissolution method was developed to simulate the gastric conditions after sleeve gastrectomy (SG) or one-anastomosis gastric bypass (OAGB), accounting for key variables (intragastric volume, pH, and contractility), and the dissolution of loratadine/desloratadine was studied pre- versus post-surgery. Dissolution was also studied after tablet crushing or syrup ingestion, as these actions are recommended after bariatric surgery. Finally, these experimental data were implemented in a newly developed physiologically based pharmacokinetic (PBPK) model to simulate loratadine/desloratadine PK profiles pre- versus post-surgery. For both drugs, pH-dependent solubility was demonstrated, with decreased solubility at higher pH; over the pH range 1-7, loratadine solubility decreased ∼2000-fold, and desloratadine decreased ∼120-fold. Ex vivo solubility in aspirated human gastric fluid pre- versus post-surgery was in good agreement with these in vitro results and revealed that while desloratadine solubility still allows complete dissolution post-surgery, loratadine solubility post-surgery is much lower than the threshold required for the complete dissolution of the drug dose. Indeed, severely hampered loratadine dissolution was revealed, dropping from 100% pre-surgery to only 3 and 1% post-SG and post-OAGB, respectively. Tablet crushing did not increase loratadine dissolution in any post-bariatric condition, nor did loratadine syrup in post-OAGB (pH 7) media, while in post-laparoscopic SG conditions (pH 5), the syrup provided partial improvement of up to 40% dissolution. Desloratadine exhibited quick and complete dissolution across all pre-/post-surgery conditions. PBPK simulations revealed pronounced impaired absorption of loratadine post-surgery, with 84-88% decreased C_max, 28-36% decreased F_a, and 24-31% decreased overall bioavailability, depending on the type of bariatric procedure. Desloratadine absorption remained unchanged post-surgery. We propose that desloratadine should be preferred over loratadine in bariatric patients, and as loratadine is an over-the-counter medication, antiallergic therapy after bariatric surgery requires special attention by patients and clinicians alike. This mechanistic approach that reveals potential post-surgery complexity, and at the same time provides adequate substitutions, may contribute to better pharmacotherapy and overall patient care after bariatric surgery.

Insights into the mapping of green synthesis conditions for ZnO nanoparticles and their toxicokinetics

Research on ZnO nanoparticles (NPs) has broad medical applications. However, the green synthesis of ZnO NPs involves a wide range of properties requiring optimization. ZnO NPs show toxicity at lower doses. This toxicity is a function of NP properties and pharmacokinetics. Moreover, NP toxicity and pharmacokinetics are affected by the species type and age of the animals tested. Physiologically based pharmacokinetic (PBPK) modeling offers a mechanistic platform to scrutinize the colligative effect of the interplay between these factors, which reduces the need for in vivo studies. This review provides a guide to choosing green synthesis conditions that result in minimal toxicity using a mechanistic tool, namely PBPK modeling.

Material Engineering in Gut Microbiome and Human Health

Tremendous progress has been made in the past decade regarding our understanding of the gut microbiome's role in human health. Currently, however, a comprehensive and focused review marrying the two distinct fields of gut microbiome and material research is lacking. To bridge the gap, the current paper discusses critical aspects of the rapidly emerging research topic of "material engineering in the gut microbiome and human health." By engaging scientists with diverse backgrounds in biomaterials, gut-microbiome axis, neuroscience, synthetic biology, tissue engineering, and biosensing in a dialogue, our goal is to accelerate the development of research tools for gut microbiome research and the development of therapeutics that target the gut microbiome. For this purpose, state-of-the-art knowledge is presented here on biomaterial technologies that facilitate the study, analysis, and manipulation of the gut microbiome, including intestinal organoids, gut-on-chip models, hydrogels for spatial mapping of gut microbiome compositions, microbiome biosensors, and oral bacteria delivery systems. In addition, a discussion is provided regarding the microbiome-gut-brain axis and the critical roles that biomaterials can play to investigate and regulate the axis. Lastly, perspectives are provided regarding future directions on how to develop and use novel biomaterials in gut microbiome research, as well as essential regulatory rules in clinical translation. In this way, we hope to inspire research into future biomaterial technologies to advance gut microbiome research and gut microbiome-based theragnostics.

Prediction of Pharmacokinetics of IDP-73152 in Humans Using Physiologically-Based Pharmacokinetics

IDP-73152, a novel peptide deformylase inhibitor with an antibacterial effect against Gram-positive bacteria, is in phase I development. The objective of this study was to develop a physiologically-based pharmacokinetic model (PBPK) for IDP-73152 in animals, and to extend the model to humans. Biopharmaceutical properties of IDP-73152 are determined using in vitro/in vivo experimentations for the PBPK model. A transit model consisting of gastrointestinal segments is applied for an estimation of the intestinal absorption kinetics. The PBPK model of IDP-73152 in rats is able to appropriately predict the plasma concentration–time profiles after the administration of IDP-73152 at different doses and by different routes (combined absolute average fold error (cAAFE), 1.77). The model is also found to be adequate in predicting the plasma concentration–time profiles of IDP-73152 in mice (cAAFE 1.59) and dogs (cAAFE 1.42). Assuming the oral administration of IDP-73152 to humans at doses of 640 and 1280 mg, the model is able to reproduce the concentration–time profiles obtained in humans (cAAFE 1.38); therefore, these observations indicate that the PBPK model used for IDP-73152 is applicable to animal species and humans. This model may be useful in predicting efficacious doses of IDP-73152 for the management of infectious disease in humans.

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.

Integration of Engineered Delivery with the Pharmacokinetics of Medical Candidates via Physiology-Based Pharmacokinetics

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic computational model that can be used to predict a drug product's ADME (absorption, distribution, metabolism, and excretion) and pharmacokinetics (PK). In recent years, PBPK modeling and simulation has been used increasingly to address many biopharmaceutics and clinical pharmacology questions, such as the effect of formulations, intrinsic factors (age, organ dysfunction, etc.), and extrinsic factors (comedications, food) on the PK of an investigational drug product. In this chapter, we will briefly introduce various PBPK models for ADME prediction and general procedures for PBPK modeling and simulations. The readers are encouraged to read updated literature on new applications of PBPK modeling and simulation which is still an emerging area in pharmaceutical development.

DallphinAtoM: Physiologically based pharmacokinetics software predicting human PK parameters based on physicochemical properties, in vitro and animal in vivo data

BACKGROUND AND OBJECTIVES

In silico experiments and simulations using physiologically based pharmacokinetic (PBPK) and allometric approaches have played an important role in pharmaceutical research and drug development. These methods integrate diverse data from preclinical and clinical development, and have been widely applied to in vitro-in vivo extrapolation (IVIVE) of absorption, distribution, metabolism, and excretion (ADME).

METHODS

To develop a user-friendly open tool predicting human PK, we assessed various references on PBPK and allometric methods published so far. They were integrated into a software system named "DallphinAtoM" (Drugs with ALLometry and PHysiology Inside-Animal to huMan), which has a user-friendly platform that can handle complex PBPK models and allometric models with a relatively small amount of essential information of the drug. The models of DallphinAtoM support the integration of data gained during the nonclinical development phase, enable translation from animal to human, and allow the prediction of concentration-time profiles with predicted PK parameters.

RESULTS

We presented two illustrative applications using DallphinAtoM: (1) human PK simulation of an orally administered drug using PBPK method; and (2) simulation of intravenous infusion following a two-compartment model using the allometric scaling method.

CONCLUSIONS

We conclude that this is a straightforward and transparent tool allowing fast and reliable human PK simulation based on the latest knowledge on biochemical processes and physiology and provides valuable information for decision making during the early-phase drug development.

Simulation Models for Prediction of Bioavailability of Medicinal Drugs-the Interface Between Experiment and Computation

The oral drug bioavailability (BA) problems have remained inevitable over the years, impairing drug efficacy and indirectly leading to eventual human morbidity and mortality. However, some conventional lab-based methods improve drug absorption leading to enhanced BA, and the recent experimental techniques are up-and-coming. Nevertheless, some have inherent drawbacks in improving the efficacy of poorly insoluble and low impermeable drugs. Drug BA and strategies to overcome these challenges were briefly highlighted. This review has significantly unravelled the different computational models for studying and predicting drug bioavailability. Several computational approaches provide mechanistic insights into the oral drug delivery system simulation of descriptors like solubility, permeability, transport protein-ligand interactions, and molecular structures. The in silico techniques have long been known still are just being applied to unravel drug bioavailability issues. Many publications have reported novel applications of the computational models towards achieving improved drug BA, including predicting gastrointestinal tract (GIT) drug absorption properties and passive intestinal membrane permeability, thus maximizing time and resources. Also, the classical molecular simulation models for free solvation energies of soluble-related processes such as solubilization, dissolutions, supersaturation, and precipitation have been used in virtual screening studies. A few of the tools are GastroPlus^TM that supports biowaiver for drugs, mainly BCS class III and predicts drug compounds' absorption and pharmacokinetic process; SimCyp® simulator for mechanistic modelling and simulation of drug formulation processes; pharmacodynamics analysis on non-linear mixed-effects modelling; and mathematical models, predicting absorption potential/maximum absorption dose. This review provides in silico-experiment annexation in the drug bioavailability enhancement, possible insights that lead to critical opinion on the applications and reliability of the various in silico models as a growing tool for drug development and discovery, thus accelerating drug development processes.

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.

In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data

BACKGROUNDS AND OBJECTIVES

In silico methods which can generate high-quality physiologically based pharmacokinetic (PBPK) models for arbitrary drug candidates are greatly needed to select developable drug candidates that escape drug attrition because of the poor pharmacokinetic profile. The purpose of this study is to develop a novel protocol to preliminarily predict the concentration profile of a target drug based on the PBPK model of a structurally similar template drug by combining two software platforms for PBPK modeling, the SimCYP simulator and ADMET Predictor.

METHODS

The method was evaluated by utilizing 13 drug pairs from 18 drugs in the built-in database of the SimCYP software. All drug pairs have Tanimoto scores (TS) no less than 0.5. As each drug in a drug pair can serve as both target and template, 26 sets were studied in this work. Three versions (V1, V2 and V3) of models for the target drug were constructed by replacing the corresponding parameters of the template drug step by step with those predicted by ADMET Predictor for the target drug. V1 represents the replacement of molecular weight (MW), V2 includes the replacement of parameter MW, fraction unbound in plasma (f_u), blood-to-plasma partition ratio (B/P), logarithm of the octanol-buffer partition coefficient (log P_o:w) and acid dissociation constant (pK_a). In V3, all above-mentioned parameters as well as human jejunum effective permeability (P_eff), V_d and cytochrome P450 (CYP) metabolism parameters (K_m, V_max or CL_int) are modified. Normalized root mean square error (NRMSE) was used for the evaluation of the model performance.

RESULTS

We found that the performance of the three versions of the models depends on structural similarity of the drug pairs. For Group I drug pairs (TS ≤ 0.7), V2 and V3 performed better than V1 in terms of NRMSE; for Group II drug pairs (0.7 < TS ≤ 0.9), 8 out of 10 V3 models had NRMSE < 0.2, the cutoff we applied to judge whether the simulated concentration-time (C-T) curve was satisfactory or not. V3 outperformed the V1 and V2 versions. For the two drug pairs belonging to Group III (TS > 0.9), V2 outperformed V1 and V3, suggesting more unnecessary replacement can lower the performance of PBPK models. We also investigated how the prediction accuracy of ADMET Predictor as well as its collaboration with SimCYP influences the quality of PBPK models constructed using SimCYP.

CONCLUSION

In conclusion, we generated practical guidance on applying two mainstream software packages, ADMET Predictor and SimCYP, to construct PBPK models for drugs or drug candidates that lack ADME parameters in model construction.

Lilly Absorption Modeling Platform: A Tool for Early Absorption Assessment

Oral drug absorption modeling has developed at a rapid pace in the 40 years or so since the first ideas for mathematical approaches to oral absorption were introduced. The success of compartmental approaches accelerated the uptake of absorption modeling, and over the last 20 years, work on absorption modeling has shifted almost exclusively to the compartmental framework. This report describes a new noncompartmental absorption modeling framework, the Lilly Absorption Modeling Platform (LAMP). LAMP connects a well-mixed stomach to a continuous tube model of the small intestine with plug flow. Within the continuous tube framework, the model includes intestinal mixing and a novel highly tunable precipitation model that can describe a combination of rapid nucleation and slow growth. The framework is designed to balance speed, consistency, and ease of use with a minimum of model complexity to capture the essential features of gastrointestinal (GI) physiology and critical elements of the oral absorption process. The model was validated based on predictions of the fraction absorbed and the maximum absorbable dose for a set of Eli Lilly and Company clinical compounds.

In-Silico Prediction of Oral Drug Bioavailability: A multi-boluses approach

This work focuses on a new mathematical model able to describe in a simple manner the intestinal physiology, in order to better study drug absorption and bioavailability. The aim of our model is to overcome the limitations of physiological pharmacokinetics models of the literature, introducing a different modelling approach. The core of the new proposed model is a Discrete-Continuous Approach (DCA): a sequence of boluses travels in the investigated portion of the intestine, in counter-current with blood that flows in continuous mode. No empirical equations are implemented in this model. Simulation results show an excellent correlation between the predicted and experimental concentration profile used to validate our model. Our new approach provides a simple tool, with a good reliability, to analyze a very complex phenomenon, using only few parameters.

Pharmacokinetics and Disposition of Heparin-Binding Growth Factor Midkine Antisense Oligonucleotide Nanoliposomes in Experimental Animal Species and Prediction of Human Pharmacokinetics Using a Physiologically Based Pharmacokinetic Model

Encapsulating the antisense oligonucleotide drug MK-ASODN with nanoliposomes greatly improved its potency and targeting to the heparin-binding growth factor midkine. The disposition and pharmacokinetic (PK) parameters of MK-ASODN nanoliposomes were studied in monkeys and rats, and the human PK parameters were predicted based on preclinical data using a physiologically based pharmacokinetic (PBPK) model. Following intravenous injection, the drug plasma concentration rapidly declined in a multiexponential manner, and the drug was rapidly transferred to tissues from the circulation. The terminal t_1/2 in plasma was clearly longer than that of the unmodified antisense nucleic acid drug. According to the AUC,MK-ASODN nanoliposomes were mainly distributed in the kidney, spleen, and liver. . MK-ASODN nanoliposomes were highly plasma protein bound, limiting their urinary excretion. Very little MK-ASODN nanoliposomes were detected in urine or feces. The plasma disposition of MK-ASODN nanoliposomes appeared nonlinear over the studied dose range of 11.5–46 mg kg⁻¹. The monkey PBPK model of MK-ASODN nanoliposomes was well established and successfully extrapolated to predict MK-ASODN nanoliposome PK in humans. These disposition and PK data support further development in phase I clinical studies.

Artemisinin Cocrystals for Bioavailability Enhancement. Part 2: In Vivo Bioavailability and Physiologically Based Pharmacokinetic Modeling

We report the evaluation and prediction of the pharmacokinetic (PK) performance of artemisinin (ART) cocrystal formulations, that is, 1:1 artemisinin/orcinol (ART-ORC) and 2:1 artemisinin/resorcinol (ART²-RES), using in vivo murine animal and physiologically based pharmacokinetic (PBPK) models. The efficacy of the ART cocrystal formulations along with the parent drug ART was tested in mice infected with Plasmodium berghei. When given at the same dose, the ART cocrystal formulation showed a significant reduction in parasitaemia at day 4 after infection compared to ART alone. PK parameters including C_max (maximum plasma concentration), T_max (time to C_max), and AUC (area under the curve) were obtained by determining drug concentrations in the plasma using liquid chromatography-high-resolution mass spectrometry (LC-HRMS), showing enhanced ART levels after dosage with the cocrystal formulations. The dose-response tests revealed that a significantly lower dose of the ART cocrystals in the formulation was required to achieve a similar therapeutic effect as ART alone. A PBPK model was developed using a PBPK mouse simulator to accurately predict the in vivo behavior of the cocrystal formulations by combining in vitro dissolution profiles with the properties of the parent drug ART. The study illustrated that information from classical in vitro and in vivo experimental investigations of the parent drug of ART formulations can be coupled with PBPK modeling to predict the PK parameters of an ART cocrystal formulation in an efficient manner. Therefore, the proposed modeling strategy could be used to establish in vitro and in vivo correlations for different cocrystals intended to improve dissolution properties and to support clinical candidate selection, contributing to the assessment of cocrystal developability and formulation development.

Optimized In Silico Modeling of Drug Absorption after Gastric Bypass: The Case of Metformin

Bariatric surgery is an effective treatment for severe obesity and related comorbidities, such as type II diabetes. Gastric bypass surgery shortens the length of the intestine, possibly leading to altered drug absorption. Metformin, a first-line treatment for type II diabetes, has permeability-dependent drug absorption, which may be sensitive to intestinal anatomic changes during bypass surgery, including Roux-en-Y gastric bypass (RYGB). Previous computer simulation data indicate increased metformin absorption after RYGB. In this study, we experimentally determined the region-dependent permeability of metformin, using the rat single-pass intestinal perfusion method (SPIP), which we then implemented into GastroPlus^TM to assess the contribution of our SPIP data to post-RYGB metformin absorption modeling. Previous simulations allowed a good fit with in vivo literature data on healthy and obese control subjects. However, it was revealed that for post-RYGB drug absorption predictions, simply excluding the duodenum/jejunum is insufficient, as the software underestimates the observed plasma concentrations post-RYGB. By implementing experimentally determined segmental-dependent permeabilities for metformin in the remaining segments post-surgery, GastroPlus^TM proved to fit the observed plasma concentration profile, making it a useful tool for predicting drug absorption after gastric bypass. Reliable evaluation of the parameters dictating drug absorption is required for the accurate prediction of overall absorption after bariatric surgery.

Integrated computer-aided formulation design: A case study of andrographolide/ cyclodextrin ternary formulation

Current formulation development strongly relies on trial-and-error experiments in the laboratory by pharmaceutical scientists, which is time-consuming, high cost and waste materials. This research aims to integrate various computational tools, including machine learning, molecular dynamic simulation and physiologically based absorption modeling (PBAM), to enhance andrographolide (AG) /cyclodextrins (CDs) formulation design. The lightGBM prediction model we built before was utilized to predict AG/CDs inclusion's binding free energy. AG/γ-CD inclusion complexes showed the strongest binding affinity, which was experimentally validated by the phase solubility study. The molecular dynamic simulation was used to investigate the inclusion mechanism between AG and γ-CD, which was experimentally characterized by DSC, FTIR and NMR techniques. PBAM was applied to simulate the in vivo behavior of the formulations, which were validated by cell and animal experiments. Cell experiments revealed that the presence of D-α-Tocopherol polyethylene glycol succinate (TPGS) significantly increased the intracellular uptake of AG in MDCK-MDR1 cells and the absorptive transport of AG in MDCK-MDR1 monolayers. The relative bioavailability of the AG-CD-TPGS ternary system in rats was increased to 2.6-fold and 1.59-fold compared with crude AG and commercial dropping pills, respectively. In conclusion, this is the first time to integrate various computational tools to develop a new AG-CD-TPGS ternary formulation with significant improvement of aqueous solubility, dissolution rate and bioavailability. The integrated computational tool is a novel and robust methodology to facilitate pharmaceutical formulation design.

Optimization of Personalized Amlodipine Dosing Strategies for Children Based on Pharmacokinetic Data from Chinese Male Adults and PBPK Modeling

For children, a special population who are continuously developing, a reasonable dosing strategy is the key to clinical therapy. Accurate dose predictions can help maximize efficacy and minimize pain in pediatrics. Methods: This study collected amlodipine pharmacokinetics (PK) data from 236 Chinese male adults and established a physiological pharmacokinetic (PBPK) model for adults using GastroPlus™. A PBPK model of pediatrics is constructed based on hepatic-to-body size and enzyme metabolism, used similar to the AUC_0-∞ to deduce the optimal dosage of amlodipine for children aged 1–16 years. A curve of continuous administration for 2-, 6-, 12-, 16-, and 25-year-olds and a personalized administration program for 6-year-olds were developed. Results: The results show that children could not establish uniform allometric amplification rules. The optimal doses were 0.10 mg·kg⁻¹ for ages 2–6 years and −0.0028 × Age + 0.1148 (mg/kg) for ages 7–16 years, r = 0.9941. The trend for continuous administration was consistent among different groups. In a 6-year-old child, a maintenance dose of 2.30 mg was used to increase the initial dose by 2.00 mg and the treatment dose by 1.00 mg to maintain stable plasma concentrations. Conclusions: A PBPK model based on enzyme metabolism can accurately predict the changes in the pharmacokinetic parameters of amlodipine in pediatrics. It can be used to support the optimization of clinical treatment plans in pediatrics.

Physiologically Based Biopharmaceutics Modeling to Demonstrate Virtual Bioequivalence and Bioequivalence Safe-space for Ribociclib which has Permeation Rate-controlled Absorption

A physiologically based biopharmaceutics model (PBBM) was developed to support formulation development of ribociclib, an orally bioavailable selective CDK4/6 inhibitor. Ribociclib is a weak base with moderate permeability and complete in vitro dissolution under stomach pH. GastroPlus™ was used to simulate the pharmacokinetics (PK) in healthy volunteers after capsule dosing. Simulations showed rapid, complete dissolution in human stomach without intestinal precipitation and with permeation-controlled absorption. Permeability was identified as controlling the systemic exposure. PBBM predicted bioequivalence (BE) between capsule and tablet in healthy volunteers, despite non-similarity between in vitro dissolution kinetics (f2<50). BE was verified in a clinical study. Then virtual bioequivalence (VBE) simulations predicted comparable PK in cancer patients between capsule and tablet of commercial batch, which was also confirmed in a clinical study. Finally, virtual trial simulations using virtual batches with slower dissolution were used to define an in vitro BE safe-space for tablets, where BE is expected. PBBM can identify drugs with permeability-controlled absorption for which formulation optimization can focus more on manufacturability rather than dissolution. PBBM can be used to predict BE study outcomes, define clinically relevant specification and BE safe-space, superseding dissolution similarity f2 criteria.

Lipopolysaccharide Nanosystems for the Enhancement of Oral Bioavailability

Nanosystems that incorporate both polymers and lipids have garnered attention as emerging nanotechnology approach for oral drug delivery. These hybrid systems leverage on the combined properties of polymeric and lipid-based nanocarriers while eliminating their inherent limitations. In view of the safety-related benefits of naturally occurring polymers, we have focused on systems incorporating polysaccharides and derivatives into the hybrid structure. The aim of this review is to evaluate existing biopolymers with specific focus on lipopolysaccharide hybrid systems and their advancement toward enhancing oral drug delivery. Furthermore, we shall identify future research areas that require further exploration toward achieving an optimized hybrid system for easy translation into clinical use. In this review, we have appraised formulations that combined polysaccharides/derivatives with lipids in a single nanocarrier system. These formulations were grouped into lipid-core-polysaccharide-shell systems, polysaccharide-core-lipid-shell systems, self-emulsifying lipopolysaccharide hybrid systems, and hybrid lipopolysaccharide matrix systems. In these systems, we highlighted how the polysaccharide phase enhances the oral absorption of encapsulated bioactives with regard to their function and mechanism. The various lipopolysaccharide designs presented in this review demonstrated significant improvement in pharmacokinetics of bioactives. A multitude of studies found lipopolysaccharide hybrid systems as nascent nanoplatforms for the oral delivery of challenging bioactives due to features that favor gastrointestinal absorption and bioavailability improvement. With future research already geared toward product optimization and scaling up processes, as well as detailed pharmacological and toxicology pre-clinical testing, these versatile systems will have remarkable impact in clinical application.

Discovery of GSK251: A Highly Potent, Highly Selective, Orally Bioavailable Inhibitor of PI3Kδ with a Novel Binding Mode

Optimization of a previously reported lead series of PI3Kδ inhibitors with a novel binding mode led to the identification of a clinical candidate compound 31 (GSK251). Removal of an embedded Ames-positive heteroaromatic amine by reversing a sulfonamide followed by locating an interaction with Trp760 led to a highly selective compound 9. Further optimization to avoid glutathione trapping, to enhance potency and selectivity, and to optimize an oral pharmacokinetic profile led to the discovery of compound 31 (GSK215) that had a low predicted daily dose (45 mg, b.i.d) and a rat toxicity profile suitable for further development.

Physiologically Based Pharmacokinetic Modelling of Inhaled Nemiralisib: Mechanistic Components for Pulmonary Absorption, Systemic Distribution, and Oral Absorption

BACKGROUND AND OBJECTIVES

Physiologically based pharmacokinetic (PBPK) modelling has evolved to accommodate different routes of drug administration and enables prediction of drug concentrations in tissues as well as plasma. The inhalation route of administration has proven successful in treating respiratory diseases but can also be used for rapid systemic delivery, holding great promise for treatment of diseases requiring systemic exposure. The objective of this work was to develop a PBPK model that predicts plasma and tissue concentrations following inhalation administration of the PI3Kδ inhibitor nemiralisib.

METHODS

A PBPK model was built in GastroPlus^® that includes a complete mechanistic description of pulmonary absorption, systemic distribution and oral absorption following inhalation administration of nemiralisib. The availability of clinical data obtained after intravenous, oral and inhalation administration enabled validation of the model with observed data and accurate assessment of pulmonary drug absorption. The PBPK model described in this study incorporates novel use of key parameters such as lung systemic absorption rate constants derived from human physiological lung blood flows, and implementation of the specific permeability-surface area product per millilitre of tissue cell volume (SpecPStc) to predict tissue distribution.

RESULTS

The inhaled PBPK model was verified using plasma and bronchoalveolar lavage fluid concentration data obtained in human subjects. Prediction of tissue concentrations using the permeability-limited systemic disposition tissue model was further validated using tissue concentration data obtained in the rat following intravenous infusion administration to steady state.

CONCLUSIONS

Fully mechanistic inhaled PBPK models such as the model described herein could be applied for cross molecule assessments with respect to lung retention and systemic exposure, both in terms of pharmacology and toxicology, and may facilitate clinical indication selection.

PBPK Modeling as a Tool for Predicting and Understanding Intestinal Metabolism of Uridine 5'-Diphospho-glucuronosyltransferase Substrates

Uridine 5′-diphospho-glucuronosyltransferases (UGTs) are expressed in the small intestines, but prediction of first-pass extraction from the related metabolism is not well studied. This work assesses physiologically based pharmacokinetic (PBPK) modeling as a tool for predicting intestinal metabolism due to UGTs in the human gastrointestinal tract. Available data for intestinal UGT expression levels and in vitro approaches that can be used to predict intestinal metabolism of UGT substrates are reviewed. Human PBPK models for UGT substrates with varying extents of UGT-mediated intestinal metabolism (lorazepam, oxazepam, naloxone, zidovudine, cabotegravir, raltegravir, and dolutegravir) have demonstrated utility for predicting the extent of intestinal metabolism. Drug–drug interactions (DDIs) of UGT1A1 substrates dolutegravir and raltegravir with UGT1A1 inhibitor atazanavir have been simulated, and the role of intestinal metabolism in these clinical DDIs examined. Utility of an in silico tool for predicting substrate specificity for UGTs is discussed. Improved in vitro tools to study metabolism for UGT compounds, such as coculture models for low clearance compounds and better understanding of optimal conditions for in vitro studies, may provide an opportunity for improved in vitro–in vivo extrapolation (IVIVE) and prospective predictions. PBPK modeling shows promise as a useful tool for predicting intestinal metabolism for UGT substrates.