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

Challenges in Predicting Colonic Luminal and Tissue Concentrations of Mesalamine and Acetyl Mesalamine using Physiologically Based Biopharmaceutics Modeling

Mesalamine is a standard first-line therapy for managing chronic inflammatory bowel diseases, such as Crohn's disease and ulcerative colitis. Despite its established efficacy, the precise mechanism of action within enterocytes remains unclear. This study aimed to develop and validate Physiologically Based Biopharmaceutics Models (PBBM) for mesalamine (5-ASA) and its metabolite, acetyl mesalamine (Ac-5-ASA), to predict drug concentrations in plasma, colonic lumen, and colonic tissue of healthy subjects and compare the results to measured concentrations. Using the Simcyp Simulator (V22), the models accurately predicted plasma concentrations for various formulations, including intravenous, oral immediate-release and controlled release formulations within a two-fold range. Results also captured the intestinal and hepatic metabolism converting mesalamine to acetyl mesalamine. However, significant discrepancies were observed in predicting luminal and tissue concentrations, with underpredictions for Claversal and Pentasa formulations reaching factors of up to 506 and 55 for 5-ASA and Ac-5-ASA in colonic tissue, respectively. These discrepancies highlight limitations in current modeling approaches, particularly in simulating drug accumulation within enterocytes. Despite these challenges, this investigation highlights both the potential benefits and the complexities of using PBBMs. Future work should focus on generating definitive N-acetyl transferase (NAT1) abundance data with an in-vitro in-vivo extrapolation link, improving approaches to better explore local drug concentrations in the gastrointestinal tract, and addressing the gap in accurately predicting luminal and tissue concentrations in the colon.

The Absorption, Distribution, Metabolism, and Excretion of Binimetinib Following a Single Oral Dose of [14C]Binimetinib 45 mg in Healthy Male Participants

Binimetinib is a MEK1/2 inhibitor particularly active in cells harboring activating mutations in the MAP kinase pathway, especially in BRAF and NRAS. Binimetinib, in combination with encorafenib, has received marketing approval in several jurisdictions for the treatment of patients with BRAF V600E or V600K mutant melanoma. The absorption, distribution, metabolism, and excretion of binimetinib were evaluated by administering a carbon 14-labeled binimetinib 45 mg dose (containing 40 μCi of radiolabeled material) to 6 healthy male participants. A total of 62.3% of the radioactivity was eliminated in the feces, while 31.4% was eliminated in the urine. The overall recovery of radioactivity in the excreta for all 6 participants was 93.6% (3.27%), indicating that good mass balance was achieved. The total percentage of the dose in the excreta of all metabolites containing the N-demethylation clearance of binimetinib by CYP1A2 and CYP2C19 was approximately 17.8%. The contribution of direct glucuronidation to the clearance of binimetinib was estimated to be 61.2% and represented the majority of the clearance. Additionally, excretion of unchanged binimetinib into the urine was estimated to have contributed 6.9% to the overall clearance. Based on study results, binimetinib is at least ≈ 50% absorbed, but based on its PK properties and because its glucuronide conjugates are unstable in the GI tract, absorption is thought to be significantly higher.

Identification of the Biotransformation Pathways of a Potential Oral Male Contraceptive, 11β-Methyl-19-Nortestosterone (11β-MNT) and Its Prodrugs: An In Vitro Study Highlights the Contribution of Polymorphic Intestinal UGT2B17

11β-Methyl-19-nortestosterone dodecylcarbonate (11β-MNTDC) is a prodrug of 11β-MNT and is being considered as a promising male oral contraceptive candidate in clinical development. However, the oral administration of 11β-MNTDC exhibits an ~200-fold lower serum concentration of 11β-MNT compared to 11β-MNTDC, resulting in the poor bioavailability of 11β-MNT. To elucidate the role of the first-pass metabolism of 11β-MNT in its poor bioavailability, we determined the biotransformation products of 11β-MNT and its prodrugs in human in vitro models. 11β-MNT and its two prodrugs 11β-MNTDC and 11β-MNT undecanoate (11β-MNTU) were incubated in cryopreserved human hepatocytes (HHs) and subjected to liquid chromatography-high resolution tandem mass spectrometry analysis, which identified ten 11β-MNT biotransformation products with dehydrogenated and glucuronidation (11β-MNTG) metabolites being the major metabolites. However, 11β-MNTG formation is highly variable and prevalent in human intestinal S9 fractions. A reaction phenotyping study of 11β-MNT using thirteen recombinant UDP-glucuronosyltransferase (UGT) enzymes confirmed the major role of UGT2B17 in 11β-MNTG formation. This was further supported by a strong correlation (R2 > 0.78) between 11β-MNTG and UGT2B17 abundance in human intestinal microsomes, human liver microsomes, and HH systems. These results suggest that 11β-MNT and its prodrugs are rapidly metabolized to 11β-MNTG by the highly polymorphic intestinal UGT2B17, which may explain the poor and variable bioavailability of the drug.

Utility of life stage-specific chemical risk assessments based on New Approach Methodologies (NAMs)

The safety assessments for chemicals targeted for use or expected to be exposed to specific life stages, including infancy, childhood, pregnancy and lactation, and geriatrics, need to account for extrapolation of data from healthy adults to these populations to assess their human health risk. However, often adequate and relevant toxicity or pharmacokinetic (PK) data of chemicals in specific life stages are not available. For such chemicals, New Approach Methodologies (NAMs), such as physiologically based pharmacokinetic (PBPK) modeling, biologically based dose response (BBDR) modeling, in vitro to in vivo extrapolation (IVIVE), etc. can be used to understand the variability of exposure and effects of chemicals in specific life stages and assess their associated risk. A life stage specific PBPK model incorporates the physiological and biochemical changes associated with each life stage and simulates their impact on the absorption, distribution, metabolism, and elimination (ADME) of these chemicals. In our review, we summarize the parameterization of life stage models based on New Approach Methodologies (NAMs) and discuss case studies that highlight the utility of a life stage based PBPK modeling for risk assessment. In addition, we discuss the utility of artificial intelligence (AI)/machine learning (ML) and other computational models, such as those based on in vitro data, as tools for estimation of relevant physiological or physicochemical parameters and selection of model. We also discuss existing gaps in the available toxicological datasets and current challenges that need to be overcome to expand the utility of NAMs for life stage-specific chemical risk assessment.

HSPiP, Computational, and Thermodynamic Model-Based Optimized Solvents for Subcutaneous Delivery of Tolterodine Tartrate and GastroPlus‑Based In Vivo Prediction in Humans: Part II

In part I, we reported Hansen solubility parameters (HSP, HSPiP program), experimental solubility at varied temperatures for TOTA delivery. Here, we studied dose volume selection, stability, pH, osmolality, dispersion, clarity, and viscosity of the explored combinations (I-VI). Ex vivo permeation and deposition studies were performed to observe relative diffusion rate from the injected site in rat skin. Confocal laser scanning microscopy (CLSM) study was conducted to support ex vivo findings. Moreover, GastroPlus predicted in vivo parameters in humans and the impact of various critical factors on pharmacokinetic parameters (PK). Immediate release product (IR) contained 60% of PEG400 whereas controlled release formulation (CR) contained PEG400 (60%), water (10%) and d-limonene (30%) to deliver 2 mg of TOTA. GastroPlus predicted the plasma drug concentration of weakly basic TOTA as function of pH (from pH 2.0 to 9). The cumulative drug permeation and drug deposition were found to be in the order as B-VI˃ C-VI˃A-VI across rat skin. This finding was further supported with CLSM. Moreover, IR and CR were predicted to achieve Cmax of 0.0038 µg/ mL and 0.00023 µg/mL, respectively, after sub-Q delivery. Added limonene in CR extended the plasma drug concentration over period of 12 h as predicted in GastroPlus. Parameters sensitivity analysis (PSA) assessment predicted that sub-Q blood flow rate is the only factor affecting PK parameters in IR formulation whereas this was insignificant for CR. Thus, sub-Q delivery CR would be promising alternative with ease of delivery to children and aged patient.

HSPiP, Computational, and Thermodynamic Model-Based Optimized Solvents for Subcutaneous Delivery of Tolterodine Tartrate and GastroPlus-Based In Vivo Prediction in Humans: Part I

Tolterodine tartrate (TOTA) is associated with adverse effect, high hepatic access, varied bioavailability, slight aqueous solubility, and short half-life after oral delivery. Hansen solubility parameters (HSP, HSPiP program), experimental solubility (T = 298.2 to 318.2 K and p = 0.1 MPa), computational (van't Hoff and Apelblat models), and thermodynamic models were used to the select solvent(s). HSPiP predicted PEG400 as the most suitable co-solvent based on HSP values (δd = 17.88, δp = 4.0, and δh = 8.8 of PEG400) and comparable to the drug (δd = 17.6, δp = 2.4, and δh = 4.6 of TOTA). The experimental mole fraction solubility of TOTA was maximum (xe = 0.0852) in PEG400 confirming the best fit of the prediction. The observed highest solubility was attributed to the δp and δh interacting forces. The activity coefficient (ϒi) was found to be increased with temperature. The higher values of r2 (linear regression coefficient) and low RMSD (root mean square deviation) indicated a good correlation between the generated "xe" data for crystalline TOTA and the explored models (modified Apelblat and van't Hoff models). TOTA solubility in "PEG400 + water mixture" was endothermic and entropy-driven. IR (immediate release product) formulation can be tailored using 60% PEG400 in buffer solution for 2 mg of TOTA in 0.25 mL (dosing volume). The isotonic binary solution was associated with a pH of 7.2 suitable for sub-Q delivery. The approach would be a promising alternative with ease of delivery to children and aged patients.

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.

Avapritinib Carries the Risk of Drug Interaction via Inhibition of UDP-Glucuronyltransferase (UGT) 1A1

BACKGROUND

Avapritinib is the only drug for adult patients with PDGFRA exon 18 mutated unresectable or metastatic gastrointestinal stromal tumor (GIST). Although avapritinib has been approved by the FDA for four years, little is known about the risk of drug-drug interactions (DDIs) via UDP-glucuronyltransferases (UGTs) inhibition.

OBJECTIVE

The aim of the present study was to systematically evaluate the inhibitory effects of avapritinib against UGTs and to quantitatively estimate its potential DDIs risk in vivo.

METHODS

Recombinant human UGTs were employed to catalyze the glucuronidation of substrates in a range of concentrations of avapritinib. The kinetics analysis was performed to evaluate the inhibition types of avapritinib against UGTs. The quantitative prediction of DDIs was done using in vitro-in vivo extrapolation (IVIVE).

RESULTS

Avapritinib had a potent competitive inhibitory effect on UGT1A1. Quantitative prediction results showed that avapritinib administered at clinical doses might result in a 14.85% increase in area under the curve (AUC) of drugs primarily cleared by UGT1A1. Moreover, the Rgut value was calculated to be 18.44.

CONCLUSION

Avapritinib has the potential to cause intestinal DDIs via the inhibition of UGT1A1. Additional attention should be paid when avapritinib is coadministered with UGT1A1 substrates.

PBPK Modelling for Drugs Cleared by Non-CYP Enzymes: State-of-the-Art and Future Perspectives

Physiologically-based pharmacokinetic (PBPK) modeling has become the established method for predicting human pharmacokinetics (PK) and drug-drug interactions (DDI). The number of drugs cleared by non-CYP enzyme metabolism has increased steadily and to date, there is no consolidated overview of PBPK modeling for drugs cleared by non-CYP enzymes. This review aims to describe the state-of-the-art for PBPK modeling for drugs cleared via non-CYP enzymes, to identify successful strategies, to describe gaps and to provide suggestion to overcome them. To this end, we conducted a detailed literature search and found 58 articles published before the 1st of January 2023 containing 95 examples of clinical PBPK models for 62 non-CYP enzyme substrates. Reviewed articles covered the drug clearance by uridine 5'-diphospho-glucuronosyltransferases (UGTs), aldehyde oxidase (AO), flavin-containing monooxygenases (FMOs), sulfotransferases (SULTs) and carboxylesterases (CES), with UGT2B7, UGT1A9, CES1, FMO3 and AO being the enzymes most frequently involved. In vitro-in vivo extrapolation (IVIVE) of intrinsic clearance and the bottom-up PBPK modeling involving non-CYP enzymes remains challenging. We observed that the middle-out modeling approach was applied in 80% of the cases, with metabolism parameters optimized in 73% of the models. Our review could not identify a standardized approach used for model optimization based on clinical data, with manual optimization employed most frequently. Successful development of models for UGT2B7, UGT1A9, CES1, and FMO3 substrates provides a foundation for other drugs metabolized by these enzymes and guides the way forward in creating PBPK models for other enzymes in these families. Significance Statement Our review charts the rise of PBPK modeling for drugs cleared by non-CYP enzymes. Analyzing 58 articles and 62 non-CYP enzyme substrates, we found that UGTs, AO, FMOs, SULTs, and CES were the main enzyme families involved and that UGT2B7, UGT1A9, CES1, FMO3 and AO are the individual enzymes with the strongest PBPK modeling precedents. Approaches established for these enzymes can now be extended to additional substrates and to drugs metabolized by enzymes that are similarly well characterized.

Drug-drug interactions that alter the exposure of glucuronidated drugs: Scope, UDP-glucuronosyltransferase (UGT) enzyme selectivity, mechanisms (inhibition and induction), and clinical significance

Drug-drug interactions (DDIs) arising from the perturbation of drug metabolising enzyme activities represent both a clinical problem and a potential economic loss for the pharmaceutical industry. DDIs involving glucuronidated drugs have historically attracted little attention and there is a perception that interactions are of minor clinical relevance. This review critically examines the scope and aetiology of DDIs that result in altered exposure of glucuronidated drugs. Interaction mechanisms, namely inhibition and induction of UDP-glucuronosyltransferase (UGT) enzymes and the potential interplay with drug transporters, are reviewed in detail, as is the clinical significance of known DDIs. Altered victim drug exposure arising from modulation of UGT enzyme activities is relatively common and, notably, the incidence and importance of UGT induction as a DDI mechanism is greater than generally believed. Numerous DDIs are clinically relevant, resulting in either loss of efficacy or an increased risk of adverse effects, necessitating dose individualisation. Several generalisations relating to the likelihood of DDIs can be drawn from the known substrate and inhibitor selectivities of UGT enzymes, highlighting the importance of comprehensive reaction phenotyping studies at an early stage of drug development. Further, rigorous assessment of the DDI liability of new chemical entities that undergo glucuronidation to a significant extent has been recommended recently by regulatory guidance. Although evidence-based approaches exist for the in vitro characterisation of UGT enzyme inhibition and induction, the availability of drugs considered appropriate for use as 'probe' substrates in clinical DDI studies is limited and this should be research priority.

The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (V_ss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of V_ss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.

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

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